The potential economic impact of electricity restructuring in the state of Oklahoma : phase II report /

ORNL/CON-485
ENGINEERING SCIENCE AND TECHNOLOGY DIVISION
THE POTENTIAL ECONOMIC IMPACT OF ELECTRICITY
RESTRUCTURING IN THE STATE OF OKLAHOMA
PHASE II REPORT
S. W. Hadley
C. R. Hudson
D. W. Jones
D. P. Vogt
October 2001
Sponsored by
The Oklahoma Corporation Commission
P.O. Box 52000-2000
Oklahoma City, OK 73152-2000
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
managed by
UT-BATTELLE, LLC
for the
U.S. DEPARTMENT OF ENERGY
under contract No. DE-AC05-00OR22725
Oklahoma Restructuring Impact iii
CONTENTS
EXECUTIVE SUMMARY.......................................................................................................v
LIST OF FIGURES AND TABLES........................................................................................ix
ACRONYMS ...........................................................................................................................xi
1 Introduction ......................................................................................................................1
2 Background .......................................................................................................................3
2.1 Recap of Phase I Analysis ...........................................................................................3
2.2 Rise of Merchant Power ..............................................................................................3
3 Oklahoma Market Data for 2010 .....................................................................................5
3.1 Demand Growth within Oklahoma ..............................................................................5
3.2 Export of Power and Transmission Capacity ...............................................................5
3.3 Generation Supply.......................................................................................................6
4 ORCED Analysis............................................................................................................. 11
4.1 Determining Production scenario...............................................................................11
4.2 Determining price scenarios ......................................................................................11
4.2.1 All plants at marginal-cost based market price ..................................................12
4.2.2 Marginal plus added fixed costs in bid price ...................................................... 14
4.2.3 Regulated pricing of existing coal and hydro plants...........................................16
4.2.4 Contracts versus spot-market pricing.................................................................17
4.3 Market power: modified bids and withheld capacity ..................................................18
4.3.1 Group bids......................................................................................................... 19
4.3.2 Withholding capacity .........................................................................................19
4.4 Elasticity and real-time pricing ..................................................................................20
5 Economic Analysis .......................................................................................................... 25
5.1 Introduction...............................................................................................................25
5.2 The Method of Analysis ............................................................................................25
5.3 Numerical Results ..................................................................................................... 27
5.4 Conclusions of input/output analysis ......................................................................... 30
6 Results and Conclusion...................................................................................................31
6.1 Excess capacity and growth in exports....................................................................... 31
6.2 Regulation of Coal and Hydro ...................................................................................32
6.3 Impact of Market Power............................................................................................32
6.4 Impact of Price Elasticity ..........................................................................................33
6.5 Economic impact on state..........................................................................................34
6.6 Conclusions............................................................................................................... 34
7 References .......................................................................................................................35
Oklahoma Restructuring Impact v
EXECUTIVE SUMMARY
Because of the recent experiences of several states undergoing restructuring (e.g., higher
prices, greater volatility, lower reliability), concerns have been raised in states currently
considering restructuring as to whether their systems are equally vulnerable. Factors such as
local generation costs, transmission constraints, market concentration, and market design can all
play a role in the success or failure of the market. These factors along with the mix of generation
capacity supplying the state will influence the relative prices paid by consumers.
The purpose of this project is to provide a model and process to evaluate the potential price
and economic impacts of restructuring the Oklahoma electric industry. The Phase I report
concentrated on providing an analysis of the Oklahoma system in the near-term, using only
present generation resources and customer demands. This Phase II study analyzed the Oklahoma
power market in 2010, incorporating the potential of new generation resources and customer
responses.
Five key findings of this Phase II were made:
• Projected expansion in generating capacity exceeds by over 3,000 MW the demands within
the state plus the amount that could be exported with the current transmission system.
• Even with reduced new plant construction, most new plants could lose money (although
residential consumers would see lower rates) unless they have sufficient market power to raise
their prices without losing significant market share (Figure S-1).
• If new plants can raise prices to stay profitable, existing low-cost coal and hydro plants will
have very high profits. Average prices to customers could be 5% to 25% higher than regulated
rates (Figure S-1). If the coal and hydro plants are priced at cost-based rates (through long-term
contracts or continued regulation) while all other plants use market-based rates then
prices are lower.
• Customer response to real-time prices can lower the peak capacity requirements by around
9%, lowering the need for new capacity and reduce prices during the peak demand.
• Changes to electric prices on the order of 5% to 20% will have only a modest effect on overall
economic activity within the state.
SUPPLY AND DEMAND
The total of existing and proposed capacity equals 25,690 MW, with a large fraction of that
being new combined cycle plants to be built in the next four years or so (Table S-1). Consumer
demands within the state are projected to grow 26% by 2010, totaling 14,340 MW. Simple
expansion of current exports would set their peak at 990 MW. Total exports could increase
greatly, limited by the maximum capacity of transmission lines exiting the state at approximately
6,050 MW. This would give a total peak demand of 20,390 MW. Dividing capacity by peak
Oklahoma Restructuring Impact vi
demand gives a reserve margin if all plants are built of 26% in 2010 and even higher in earlier
years. While some reserve is required for reliability reasons, such a high level of excess capacity
is not sustainable in a restructured market.
Table S-1: Projected electricity supply and demand for Oklahoma in 2010.
Supply, MW Demand, MW
Existing Plants 12,170 Residential 7,510
New Combined Cycle 11,850 Commercial 3,350
New Combustion Turbine 1,670 Industrial 2,910
Other 570
Max exports 6,050
Total 25,690 Total 20,390
In fact, there is a growing realization that the market may be set for a bust in the near future.
According to Christopher Ellinghaus, an investment banker at Williams Capital Group, power
companies across the country have proposed 350,000 MW of new plants by 2004, but only
100,000 MW of this is actually expected to be built (Bannerjee, 2001). According to the New
York Times article, transmission constraints and power plant economics are both playing a role
in the lowering of expectations. Many of the announcements of new capacity were based on the
expectation of broadly rising prices, as exemplified by California and the entire western region.
With the recent decline in wholesale prices, new plant economics are not as favorable.
Furthermore, many of the plants are being located in states with large gas resources, such as
Oklahoma, Texas, and Louisiana. However, transmission systems are not being upgraded
quickly enough to be able to ship this excess capacity to states needing it. In Oklahoma, only one
additional 345 kV line is planned between now and 2010. Expansion of the transmission system
is more difficult to construct than new generation. Current transmission owners see little benefit
to build since it dilutes the value of their existing lines and/or regulated returns are low. Owners
of plants in high-cost regions may also prefer constraints that keep low-cost power out.
Landowners do not see the benefit since the power is to be used by others far away. Even
intervening states frequently object to new lines. For example, Connecticut recently vetoed a
needed transmission line to Long Island (Behr 2001).
MARKET PRICING AND PLANT PROFITABILITY
In a purely competitive market where supply bids into a market until demand is satisfied, the
optimum bid for any supplier is to price their product at its marginal cost. Prices then are set by
the highest price bid that fulfills demand. The problem for the electric industry is that in an
industry with a high ratio of fixed to variable costs, there is a greater likelihood that the resulting
prices will not cover their fixed costs, leading to boom and bust cycles. Examples include such
industries as airlines, steel, and cement. In many such industries, what happens is a shortage that
boosts profits, leading to a build-up of capacity that then leads to temporary cutbacks as demand
and supply constantly equilibrate. The lack of an economical electrical storage mechanism and
the large sizes of plants makes this process potentially even more of a problem for the electric
generation business. The inelasticity of supply and demand can lead to great volatility.
Oklahoma Restructuring Impact vii
Average prices for the modeled Oklahoma market under regulated rates and with all plants
pricing at the market are shown in Figure S-1. However, in this scenario most new plants lose
money. If plants could raise prices by adding some of their fixed costs into their price, they
become profitable but prices rise for all consumers.
Figure S-1: Consumer prices under regulation and with different market scenarios.
0
1
2
3
4
5
6
7
8
9
10
Residential Commercial Industrial
¢/kWh
0
1
2
3
4
5
6
7
8
9
10
Regulated Generation Price
Market w/ all at marginal cost
Market w/ fix cost adder
Market w/ adder but Coal and Hydro Regulated
T&D
Plants are very reliant on the existence of higher-priced plants in order to make their profits in
a spot market. Even if a large segment of the capacity raises its price, it risks being undercut by
other plants unless they bid to just below the others’ marginal cost. The incentive for individual
plants to “cheat” and lower their bids can undermine the market power potential. Only if a
substantial majority of the participants in the market, especially those with higher costs, raise
their bids proportionately, do profits rise for all.
Withholding capacity can be successful in increasing profits, but only if the market is
constrained so that other producers (internal or external to the state) cannot offset the capacity
except at higher prices. Long-term contracts can mitigate the volatility of spot markets, with
prices likely approaching the regulated rates. In the actual market, companies may choose to sell
some of their generation under long-term contracts, some on the day-ahead or spot market, some
as either a spinning or non-spinning reserve, as well as save some for internal use if it is a
cogeneration project. All of these factors influence the final market and prices to consumers.
Oklahoma Restructuring Impact viii
REGULATION OF EXISTING LOW-COST PLANTS
Among the major beneficiaries of a change to pricing using market-based prices are the
existing low-cost producers, notably coal and hydro facilities. These two plant types would
receive prices much higher than their average costs plus a reasonable return. It might be feasible
during restructuring to mandate that they sell their power at cost plus a reasonable profit, instead
of at the full market rates. There are precedents of this in other states. For example, as part of its
restructuring, California required that the nuclear and hydro facilities owned by the investor-owned
utilities price their production at cost. While the rest of the production in the state became
very expensive this past year, the nuclear and hydro plants provided some measure of stability.
The utilities in many states undergoing restructuring have been faced with the problem of
paying for power plants that were more expensive than the market would bear. Oklahoma is
faced with the opposite situation; many of its existing power plants, especially the coal and hydro
plants, have costs much lower than the expected market prices. It may be advisable that some or
all of this difference be returned to customers in some fashion, through mechanisms such as
continued cost-based pricing (as we modeled), rebates following the sale of plants, or other
mechanisms. Figure S-1 shows the price impact if these plants continue to price based on their
regulated costs.
RESPONSE OF CUSTOMERS TO REAL-TIME PRICES
Customer response to high peak prices lowered the peak demand by roughly 9% in our model,
lessening the need for new capacity. The response of customers to real-time prices has a modest
effect on average prices paid. Its larger impact is on prices paid at the peak. In the case without
elasticity impacts, market prices were 120 ¢/kWh during the short time when all plants were at
full capacity. In the case with elasticity this price peaked at 100 ¢/kWh. With elasticity and
consequent flatter demand profile, peak prices do not have to rise as much to lower demand to
available capacity.
OVERALL ECONOMIC IMPACT TO STATE
The scenario with the highest price increases raised prices an average of 12 percent, in a
commodity that accounts for 2.3 percent of state production. Against this aggregate backdrop, it
is not surprising that the electricity rate changes have very small impacts on the overall economy
of the state. Depending on the price-change scenario, employment in the state could fall by three
or four one-hundredths of one percent while other property income could rise by about one-third
of one percent. The differences in impact across the scenarios also are small.
These impact projections are likely to be on the high side of actual, long-run impacts, since
the assumptions of the input-output framework, as well as assumptions we adopted for this study,
minimize the opportunities to substitute away from electricity in both final and intermediate
demands. We did not attempt to simulate the potential for substitution away from electricity into
natural gas for some energy uses, but over a five- to ten-year period, if some classes of rates
stayed twenty to twenty-five percent higher, some substitutions surely would occur in specific
uses such as heating, air conditioning and water heating.
Oklahoma Restructuring Impact ix
LIST OF FIGURES AND TABLES
Figure S-1: Consumer prices under regulation and with different market scenarios ...................vii
Figure 1: Oklahoma transmission lines with 345 kV lines highlighted (SPP 2001) ......................6
Figure 2: Oklahoma electricity supply curves for 1999 and 2010 ................................................9
Figure 3: Customer prices under each market scenario..............................................................13
Figure 4: Real-time market prices with all plants bidding their marginal costs ..........................14
Figure 5: Real-time market prices with plants adding 25% of fixed costs to bids.......................16
Figure 6: Change in Residential Load Duration Curve from real-time pricing and elasticity......22
Table S-1: Projected electricity supply and demand for Oklahoma in 2010. ...............................vi
Table 1: July 2001 listing of proposed plants for Oklahoma (DEQ 2001)....................................4
Table 2: Oklahoma electricity demand growth from 1999 to 2010 ..............................................5
Table 3: Estimated interstate transmission capacity from Oklahoma in 2010...............................6
Table 4: New Generating plants from Oklahoma DEQ and RDI..................................................7
Table 5: New plant cost and operating parameters.......................................................................8
Table 6: Average fuel prices for 2010, in 1999 $/MBtu...............................................................8
Table 7: Consumer regulated and market-based prices with plants bid marginal costs only .......12
Table 8: Plant operations and financial results with prices based on marginal costs...................13
Table 9: Operating and financial results with all plants at market rates and bids include 25% of
fixed costs .........................................................................................................................15
Table 10: Prices with all plants at market rates and bids include 25% of fixed costs..................16
Table 11: Prices with market prices including 25% fixed cost but existing coal and hydro plants
priced at costs ...................................................................................................................17
Table 12: Percent increase in prices market-based versus regulated, with most or all plants at
market rates.......................................................................................................................27
Table 13: Industrial sectors with output decreases, percents ...................................................... 28
Table 14: Industrial sectors with output increases, percents....................................................... 29
Table 15: Economic impacts of changes in electricity price schedules, percent change..............30
Oklahoma Restructuring Impact xi
ACRONYMS
CC Combined Cycle
CT Combustion Turbine
DEQ Oklahoma Department of Environmental Quality
EIA Energy Information Administration
EPRI Electric Power Research Institute
FERC Federal Energy Regulatory Commission
NERC North American Electric Reliability Council
O&M Operations and Maintenance
OCC Oklahoma Corporation Commission
ORCED Oak Ridge Competitive Electricity Dispatch model
ORNL Oak Ridge National Laboratory
RDI Resource Data International
ROE Return on Equity
SPP Southwest Power Pool
T&D Transmission and Distribution
Oklahoma Restructuring Impact 1
1 Introduction
In April 1997, the Oklahoma legislature passed a bill to restructure the state’s electric
industry, requiring that the generation sector be deregulated and allowing retail competition by
July 2002. Details of the market structure were to be established later. Senate Bill #220,
introduced in the 2000 legislature, provided additional details on this market, but the bill did not
pass. Subsequent discussions have identified the need for an objective analysis of the impact of
restructuring on electricity prices and the state’s economy, especially considering the experiences
of other states following restructuring of their electric systems.
Because of the recent experiences of other states undergoing restructuring (e.g., higher prices,
greater volatility, lower reliability), concerns have been raised in states currently considering
restructuring as to whether their systems are equally vulnerable. Factors such as local generation
costs, transmission constraints, market concentration, and market design can all play a role in the
success or failure of the market. Energy and ancillary services markets both play a role in having
a well-functioning system. Customer responsiveness to market signals can enhance the flexibility
of the market.
The purpose of this project is to provide a model and process to evaluate the potential price
and economic impacts of restructuring the Oklahoma electric industry. The goal is to provide
sufficient objective analysis to the Oklahoma legislature that they may make a more informed
decision on the timing and details of any future restructuring. It will also serve to inform other
stakeholders on the economic issues surrounding restructuring. The project is being conducted in
two phases. The Phase I report (Hadley 2001) concentrated on providing an analysis of the
Oklahoma system in the near-term, using only present generation and transmission resources.
This Phase II report looks further in the future, incorporating the potential of new generation
resources. Changes in the market structure due to additional capacity, pricing mechanisms, and
export markets are considered.
During the initial phase of the analysis, Oak Ridge National Laboratory (ORNL) developed a
benchmark or base case based on the existing set of plants, customer demands, and regulated
power prices. Generation and electric market data were gathered from the Department of
Energy’s Energy Information Administration (EIA), Resource Data International (RDI), the
North American Electric Reliability Council (NERC), and the Oklahoma Corporation
Commission (OCC). An ORNL-specialized model, the Oak Ridge Competitive Electricity
Dispatch (ORCED) model, was used to evaluate the marginal-cost-based prices for the state.
In this second phase of the study, we advanced the supplies and demands amounts to model
the year 2010. We considered the potential expansion of the electricity export market as
constrained by the available transmission capacity. Resulting power prices were adjusted to show
the impact of market power in bidding and the continued regulation of some power plants. Using
the real-time prices, we adjusted customer load profiles based on their price elasticity and
reevaluated the impact of restructuring on consumer prices. Lastly, we used an input/output
economic simulation of the Oklahoma economy to determine the broader economic impacts of
changes in prices.
Oklahoma Restructuring Impact 3
2 Background
2.1 Recap of Phase I Analysis
The Phase I study provided a view of the Oklahoma electricity market if restructuring
occurred in 1999. Customer demands and power plant production were found from existing
reports submitted to the Federal Energy Regulatory Commission (FERC) and Energy
Information Administration (EIA). Existing plants were allowed to price based on the marginal
cost of the highest-cost plant operating at any one time.
The analysis identified two key issues. First, much of the existing capacity is low-cost coal.
Under existing regulated pricing these plants receive revenues sufficient to pay costs plus a
reasonable return on investment. In a restructured market with prices set by the marginal
producer, revenues for the low-cost coal plants increased greatly. This was reflected in a general
rise in electricity prices of around 1¢/kWh in the base scenario. Furthermore, market-based
electricity prices are more sensitive to the price of natural gas. With an increase in gas prices of
53%, market prices rose 1.5¢/kWh while regulated prices (that average all production costs) rose
0.5¢/kWh. As a consequence, market prices became 2¢/kWh higher than regulated prices.
Sensitivities were also run on the availability of coal-fired capacity, raising it from the
historical value of the existing plants to broader industry standards. The increase in low-cost
production lowered both the regulated and market prices. An interesting detail was that the
increased production from the coal plants actually lowered their profitability because of their
effect on market prices. Other plants also suffered lower profits, threatening their continued
operation. This touches on the issue of market power, which will be looked at in more detail in
this paper.
2.2 Rise of Merchant Power
Generation capacity is growing throughout the country. According to the RDI NewGen
database (RDI 2001b), over 390 GW of capacity are planned or under construction in the U.S.
Much of this capacity is being built not by regulated utilities, but by independent power
producers. These producers sell their generation either through long-term contracts to utilities or
in shorter-term or spot markets. Within Oklahoma, 98% of the proposed new construction is by
merchant power producers.
As part of restructuring, power plants may sell directly to end-use consumers. As with
utilities, consumers may sign bilateral long-term contracts or purchase through a spot market.
Small consumers may choose to aggregate their demands to better take advantage of the market.
These aggregators may be existing utilities, municipalities, or even new organizations that
provide this service.
Even without restructuring in all states, merchant power is a rising force within the electric
power industry. Traditional utilities have been reluctant to construct new facilities, due to
uncertainty of the market and potentially inadequate returns on their investment. Some utilities
have created unregulated subsidiaries to control their generation assets and to build additional
plants. They choose to use their expertise in owning and operating power plants by competing in
Oklahoma Restructuring Impact 4
the open market outside of their regulated territories. Other companies have also entered the
market, building either stand-alone merchant plants or cogeneration plants within an existing
industrial facility.
In some states that are undergoing restructuring, the utilities have been forced to sell some or
all of their generation. This was done to avoid the utilities obtaining too much market power
through combined ownership of transmission assets and a large share of the generation assets.
Auctions have been held to sell the plants to multiple companies. The prices paid helped to
determine the asset values and stranded costs of the utility. These plants have frequently been
purchased by the unregulated subsidiaries of utilities that are located elsewhere in the country or
overseas, or by independent power producers.
In Oklahoma, there have been a large number of plants proposed for construction in the
coming years. The Oklahoma Department of Environmental Quality (DEQ 2001) releases a
monthly report of the proposed plants that shows the air permit status, capacity, and type of plant
(Table 1). Most of these plants are to be built by companies that are not the regulated utilities
within the state. As such they will be able to sell their power either through contracts with
existing utilities, on the wholesale spot market, or if restructuring occurs, directly to consumers.
Table 1: July 2001 listing of proposed plants for Oklahoma (DEQ 2001)
Facility Permit
Status
Fuel Gen. Cap.
MW
Base Units(Comb.Cycle)
AECI – Chouteau Issued GAS 530
Cogentrix- Jenks Issued GAS 800
C&SW – Oologah Issued GAS 492
Calpine – Coweta Issued GAS 1,000
Duke – Newcastle Issued GAS 520
Energetix-Arcadia Proposed GAS 1,100
Energetix Thunderbird Issued GAS 865
Kiowa – Kiamichi Issued GAS 1,200
Smithcogen – Pocola Proposed GAS 1,200
Smithcogen. - Lawton Tech. Rev. GAS 600
Energetix – Webbers Falls Tech. Rev. GAS 825
Tenaska - Seminole Tech. Rev. GAS 1,200
Energetix – Great Plains Tech. Rev. GAS 900
Duke - Stephens Admin Rev GAS 620
Total Combined Cycle 11,852
Peaking Units(Simp. Cycle)
OG&E - Horseshoe Issued GAS 90
OneOK - Edmond Issued GAS 320
KM Pwr - Pittsburg Issued GAS 550
WFEC – Anadarko Issued GAS 94
Mustang – Mustang Draft GAS 310
Mustang - Harrah Admin Rev GAS 310
Total Simple Cycle 1,674
Grand Totals 13,526
Oklahoma Restructuring Impact 5
3 Oklahoma Market Data for 2010
3.1 Demand Growth within Oklahoma
According to the Electric Power Annual 1999 (EIA 1999), total retail demand for Oklahoma
in 1999 was 46,700 GWh. According to the EIA’s Annual Energy Outlook 2001 (EIA 2000),
overall electric power demand in the Southwest Power Pool is expected to grow overall by 26%
between 1999 and 2010, representing an annual growth rate of 2.1% (Table 2). Each sector
(residential, commercial, and industrial) has different levels of growth, depending on a variety of
factors such as economic development and changes in technology.
Table 2: Oklahoma electricity demand growth from 1999 to 2010
1999 Sales
GWh
Annual
escalation
2010 Sales
GWh
Losses Busbar
GWh
Peak
MW
Residential 18,300 2.2% 23,400 8% 25,400 7,510
Commercial 12,400 2.6% 16,500 6% 17,500 3,350
Industrial 13,300 1.4% 15,500 5% 16,300 2,910
Other 2,800 2.1% 3,500 6% 3,700 570
Total 46,700 2.1% 58,900 63,000 14,340
3.2 Export of Power and Transmission Capacity
Based on the analysis in Phase I, total exports of power from Oklahoma in 1999 were 4,800
GWh, with a peak demand of 800 MW. A simple expansion of this demand using the growth rate
from above through 2010 would give sales of 6,500 GWH and peak capacity of 990 MW.
Consequent total demand would be 15,300 MW. However, proposed expansions of capacity
greatly exceed this amount, as shown in Table 1. Since power plant capacity is projected to be
much higher, the question arises as to the how much could exports increase, given transmission
constraints.
There are currently seven 345 kV lines that cross Oklahoma state lines according to the
Southwest Power Pool (SPP) (Figure 1).1 An approximate line rating for conventional three-phase
lines at 345 kV is 600 MW (EPRI 1982), although this value can vary greatly depending
on the length and materials used for the line. These lines could therefore accommodate 4,200
MW of interstate transmission. An additional 345 kV line is planned for 2006 (Northwest to
Harrington), which could provide an additional 600 MW of transmission capacity.
There is also one 230 kV line that crosses into the panhandle of Texas (Elk City to
Harrington-Nichols). The estimated capacity for this line is 200 MW. In addition, several 138 kV
lines cross the state border that could be used for interstate energy transfer. Using an
approximate line rating for 138 kV lines of 75 MW, the estimated maximum capacity at 138 kV
is 1,050 MW.
1 The seven current 345 kV lines are Woodring to Wichita, Northeastern to Neosho, GRDA 1 to Flint
Creek, Clark to Chambers Springs, Muskogee to Ft. Smith, Valliant to Lydia, and Lawton to Oklaunion.
Oklahoma Restructuring Impact 6
As shown in Table 3 below, it appears that by 2010 Oklahoma would have a maximum
interstate transmission capacity of approximately 6,000 MW. Hirst and Kirby give higher
estimates of the capacity for various voltage lines, closer to 900 MW per 345 kV line (Hirst and
Kirby 2001). However, their analysis was based on thermal limits for short lines, less than 100
miles. As lengths increase, the maximum transmission capability drops because of voltage or
stability limits. Furthermore, actual combined capabilities can be different from simply the sum
of the individual lines, and sales potentials will depend on the markets that the lines enter. A
more detailed analysis of the actual transmission limits in and out of Oklahoma may be
necessary to establish the potential for exports.
Figure 1: Oklahoma transmission lines with 345 kV lines highlighted (SPP 2001)
Table 3: Estimated interstate transmission capacity from Oklahoma in 2010
Line Voltage (kV) Number of
lines
Approximate line
rating (MW)
Estimated maximum
capacity (MW)
345 8 600 4,800
230 1 200 200
138 14 75 1,050
Total 23 - 6,050
3.3 Generation Supply
Generation capacity is growing in Oklahoma. Plans for new plants indicate a broad expansion,
roughly doubling the amount of capacity available. The number of plants listed in Phase I for
1999 totaled 13,430 MW. In 2010, most of these plants are expected to still be operating,
Oklahoma Restructuring Impact 7
although 1,260 MW of plants were removed from the list due to either retirement or
refurbishment. This left 12,170 MW of capacity from existing plants in 2010.
There were three sources of data on new plants for Oklahoma. The most complete source used
was the list of new plants from the Oklahoma DEQ (Table 1). These were compared to lists of
plants from Resource Data International’s NewGen (RDI 2001a) and PowerDat databases (RDI
2001b) that give capacity, fuel, and expected on-line date. In some cases either the plant
capacities did not match with the DEQ data, the plants did not have the same name, or on-line
dates were not provided. The DEQ capacities were used, with on-line dates of 2004 for those
without known dates. The total new capacity added was 11,852 MW of combined cycle (CC)
and 1,674 MW of combustion turbines (CT), in line with the DEQ estimates (Table 4).
Table 4: New Generating plants from Oklahoma DEQ and RDI
Facility Permit
Status
Fuel Gen. Cap.
MW
RDI Gen.
Cap. MW
RDI On-line
Date
Base Units(Comb.Cycle)
AECI – Chouteau Issued GAS 530 530 2004
Cogentrix- Jenks Issued GAS 800 800 2002
C&SW – Oologah Issued GAS 492 300 2001
Calpine – Coweta Issued GAS 1,000 1,000 2002
Duke – Newcastle Issued GAS 520 500 2001
Energetix-Arcadia Proposed GAS 1,100 1060 2003
Energetix Thunderbird Issued GAS 865 825 2003
Kiowa – Kiamichi Issued GAS 1,200 1200 2003
Smithcogen – Pocola Proposed GAS 1,200 600 2003
Smithcogen. - Lawton Tech. Rev. GAS 600 600 2003
Energetix – Webbers Falls Tech. Rev. GAS 825 825 –
Tenaska - Seminole Tech. Rev. GAS 1,200 1,200 –
Energetix – Great Plains Tech. Rev. GAS 900 500 2004
Duke - Stephens Admin Rev GAS 620 ?
Total Combined Cycle 11,852
Peaking Units(Simp. Cycle)
OG&E - Horseshoe Issued GAS 90 96 2000
OneOK - Edmond Issued GAS 320 300 2001
KM Pwr - Pittsburg Issued GAS 550 550 2003
WFEC – Anadarko Issued GAS 94 90 –
Mustang – Mustang Draft GAS 310 ?
Mustang - Harrah Admin Rev GAS 310 ?
Total Simple Cycle 1,674
Grand Totals 13,526
Because the actual efficiencies and costs for these plants are not known, we used
representative values from a recent study on future energy use, Clean Energy Futures, written by
five national laboratories including ORNL (Inter-Laboratory Working Group 2000) and from the
Generating Availability Data System from NERC (NERC 1998). Heat rates, fixed and variable
Oklahoma Restructuring Impact 8
operations and maintenance (O&M) costs, capital costs were , and outage rates for combined
cycle and combustion turbines were assigned to the plants in Table 4 based on the year they
entered into service (Table 5).
Table 5: New plant cost and operating parameters
Effici-ency
Capital
Nom $/kW
Variable O&M
1999 ¢/kWh
Fixed O&M
1999 $/kW
Forced
Outage Rate
Planned
Outage Rate
Combined Cycle
2001 49.7% 476 0.05 15.6 3.4% 10.3%
2002 50.1% 491 0.05 15.6 3.4% 10.3%
2003 50.5% 505 0.05 15.6 3.4% 10.3%
2004 51.0% 521 0.05 15.6 3.4% 10.3%
Combustion turbine
2001 37.8% 354 0.01 6.4 3.0% 10.8%
2002 38.3% 365 0.01 6.4 3.0% 10.8%
2003 38.8% 376 0.01 6.4 3.0% 10.8%
2004 39.3% 387 0.01 6.4 3.0% 10.8%
Average fuel prices were raised from the 1999 values used in Phase I to 2010 values by
applying the expected increase from the AEO2001 for the Oklahoma region (Table 6). (Note that
coal prices are expected to increase at less than the general inflation rate so have a negative
escalation rate.) In Phase I, the existing plants used fuel prices based on their reported amounts
for 1999. We escalated each plant’s cost by the rate shown in Table 6. The new plants used the
average prices as shown in the table.
Table 6: Average fuel prices for 2010, in 1999 $/MBtu
1999 Avg. Fuel Cost,
$/MBtu
Annual Escalation Above
General Inflation
2010 Avg. Fuel Cost,
1999$/MBtu
Gas 2.73 1.8% 3.34
Coal 0.94 -1.0% 0.84
Dist. Oil 2.06 1.0% 2.29
Although the plants are listed as in planning or under construction, it is not known whether all
will be built. For example, three plants are proposed for construction in the Lawton, OK, area.
However, according to minutes of the Lawton city council there may be water limits requiring
auctioning of available water (Lawton City Council 2001). In scenarios where there was excess
capacity, some plants were removed from the list, beginning with those with the latest on-line
date. However, in reality, other plants may have their dates or capacities modified, or
transmission capacity may be added through new lines or modifications. Also, the costs and
operating parameters are likely to be different than the estimates used here. For these reasons, it
is important to realize that the results from this study should not be applied to specific plants.
The plants within Oklahoma were separated into 141 units, each with its own capacity and
operating parameters. When placed in order of increasing marginal cost, they create a supply
Oklahoma Restructuring Impact 9
curve. Figure 2 shows the supply curve for the plants in 2010, as well as the 1999 curve from the
Phase I report. The large increase in capacity at essentially the same marginal price has
interesting consequences for the profitability of the plants, as described later.
Figure 2: Oklahoma electricity supply curves for 1999 and 2010
0
2
4
6
8
10
12
14
0 5,000 10,000 15,000 20,000 25,000 30,000
Power Level, MW
Price, ¢/kWh
2010 Supply Curve
1999 Supply Curve
New Combined Cycle
New Combustion
Turbine
Existing Coal
Existing Gas
CT and ST
Oklahoma Restructuring Impact 11
4 ORCED Analysis
The Oak Ridge Competitive Electricity Dispatch (ORCED) model was developed at Oak
Ridge National Laboratory to examine numerous facets of a restructured electricity market
(Hadley and Hirst 1998). The model is a complex Excel spreadsheet that takes the inputs on
supply and demand described above and dispatches plants to meet the defined demands for a
single year of operation. Further details on the calculations involved are included in the Phase I
report (Hadley et al., 2001).
Several versions of the model have been developed over the years depending on the needs of
the study. For this study we used a version that models a single region without internal
transmission constraints. It can handle up to 200 power plants and models two seasons, a peak
and an off-peak. For the Phase II analysis the model was modified to allow individual plants to
sell some or all of their power at either the market-based rate or on a contractual, fixed cost basis.
Also, the calculation of the bid prices that plants use in the spot market was modified to enable
varying amounts of fixed costs to be incorporated into the bid.
4.1 Determining Production scenario
In order to create a credible scenario for 2010 we must first establish the expected demand and
production levels. For the initial run, we used the internal demands as shown in Table 2 and kept
exports equal to 11% of internal retail demands as in 1999. This resulted in a peak demand of
15,300 MW. If all planned capacity is built, the total capacity available is 25,600 MW, resulting
in a reserve margin of 67%. At such a high level, many of the plants will never be called upon to
produce, which makes this scenario unlikely. Either demands must increase, supply decrease, or
both.
To increase demands, we raised the amount of exports from a peak demand of 990 MW to
6,030 MW, as described in Section 3.2 above. We left the load factor the same as in 1999 so that
total sales also scaled up by a factor of five as well. This makes total exports equal to 67% of
retail sales, versus 11% in 1999, and raised peak demand to 20,380 MW. Even with this
expansion to full capacity of the transmission lines, the reserve margin was 25.8%. Many plants
were still never called upon or for only a few hours in the year.
We lastly lowered the supply capacity by removing 3,545 MW of planned combined cycle
construction (the last four CC plants in Table 4). This lowered the reserve margin to 8.4%, which
is slightly lower than the 10.0% in the 1999 case. While too high of a value can cause some
plants to run so rarely that they are not economic, too low of a value can cause reliability
concerns. For example, California declares Stage One Emergencies when the reserve margin
drops below 7%. Further information on this topic can be found in the paper on generation
adequacy (Hirst and Hadley 1999).
4.2 Determining price scenarios
Once supplies and demands were set, we considered the prices to customers and profitability
of the plants. There are two issues concerning prices, what are the overall prices paid by
customers in a restructured market compared to a regulated market, and do new plants receive
Oklahoma Restructuring Impact 12
sufficient revenues to justify their construction and operation. If prices are low to consumers but
new plants are losing money, then this is not a viable scenario. We set the expected return on
equity for the new combined cycle plants at 14% after taxes. Returns significantly below that
would discourage investors from funding their construction.
4.2.1 All plants at marginal-cost based market price
First, we ran a scenario that had all plants bid their marginal prices on the spot market. This is
similar to the pricing used in Phase I. This scenario provides lower prices to residential
consumers than the regulated prices, although other customers pay slightly higher prices. Table 7
first shows the transmission and distribution (T&D) charge to customers, based on results from
the Phase I study. These were not changed from the 1999 values because the focus of this study
is not on changes in T&D prices. The next column shows the generation prices if system-wide
revenue requirements are allocated between customer categories based on their energy purchases
and peak demand requirements. The third column is the sum of the previous two. The
restructured generation price is the average price paid by each customer category if charged
based on the time-varying market price, with adjustments for plants that have prices fixed by
long-term contracts. The total restructured price is the sum of the generation and T&D prices,
and the difference between restructured and regulated prices are shown in the last column. The
regulated and market prices are shown in Figure 3.
Table 7: Consumer regulated and market-based prices with plants bid marginal costs only
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.83 7.17 3.26 6.59 -0.57
Commercial 2.89 3.14 6.02 3.16 6.05 0.03
Industrial 1.00 3.03 4.03 3.13 4.13 0.10
In this scenario with all plants charging marginal costs, the new plants lost money because
they could not recover their fixed costs. The combined cycle plants had a return on equity (ROE)
of -2%, well below the expected value of 14%. In Table 8 the first column shows the capacity for
each major plant type. Hydro includes both hydro and pumped storage facilities. The “Old”
designation is for plants built prior to 2000 and “New” for plants built in 2000 or later. The
capacity factor indicates the amount of generation the plant produced as compared to if the plant
ran at full power 100% of the year. The percentage of time on the margin indicates for what
percentage of the year each plant type was the most expensive plant operating and so setting the
market-clearing price. The average price is the revenues received by each plant type divided by
its production, while the average cost is the sum of its fuel, O&M, depreciation, interest charges,
taxes, and a “reasonable” return on equity (shown in the last column). The ROE is the net
income received by the plants (escalated to 2010 $) divided by the equity invested in the plants.
Oklahoma Restructuring Impact 13
Figure 3: Customer prices under each market scenario
0
1
2
3
4
5
6
7
8
9
10
Residential Commercial Industrial
¢/kWh
0
1
2
3
4
5
6
7
8
9
10
Regulated Generation Price
Market w/ all at marginal cost
Market w/ fix cost adder
Market w/ adder but Coal and Hydro Regulated
T&D
Table 8: Plant operations and financial results with prices based on marginal costs
Plant Type
Capa-city
MW
Capa-city
Factor
%
Time
on
Margin
%
Price
Receiveda
¢/kWh
Marginal
Costb
¢/kWh
Total
Costc
¢/kWh
ROE
%
Regu-lated
ROE
%
Coal 5156 76 0 2.77 0.97 1.45 437.3 11.9
Hydro 1035 29 0 3.45 0.37 1.52 772700 0.0
Oil 39 19 0 3.53 2.69 4.39 -598.6 11.0
Gas ST 4625 5 13 5.49 3.90 6.28 -33.9 11.0
Gas CC/Old 909 29 10 3.79 3.03 3.67 41.5 12.9
Gas CT/Old 343 6 1 4.57 3.38 7.23 -93.0 11.0
Gas CC/New 8309 79 54 2.89 2.31 3.62 -2.0 14.0
Gas CT/New 1674 22 21 3.92 2.90 6.14 -4.2 13.9
a Price received is total revenue divided by sales.
b Marginal cost is variable and start-up costs of operation
c Total cost includes operations, depreciation, interest, taxes, and expected return on equity
Because there was so much new combined cycle capacity, these plants were on the margin for
54% of the year, even though they operated close to capacity with a capacity factor of 79%.
Being similar plants of similar age, their marginal cost and consequent bid prices were also
similar, at about 2.3 ¢/kWh. This can be seen in Figure 2, with the long flat part of the supply
curve showing the capacity from the new combined cycle plants.
Oklahoma Restructuring Impact 14
Though their marginal costs were only 2.3 ¢/kWh, their total cost including all operations and
capital-related costs were about 3.6 ¢/kWh. If they only received their marginal costs during the
portion of the year they were on the margin, there was very little time for them to recover all of
the fixed costs and prices would have to be much higher during that part of the year.
Figure 4 shows the market-based prices over the year if all plants solely bid their marginal
costs. As can be seen, the prices are below 3.6 ¢/kWh for most of the year. Only during about
30% of the summer peak season, or just 9% of the year, do prices rise above the total cost of new
combined cycle plants, as more expensive combustion turbines and gas-fired steam plants set the
price.
Figure 4: Real-time market prices with all plants bidding their marginal costs
0
2
4
6
8
10
12
14
0 20 40 60 80 100
Percent of Season
¢/kWH
Peak Season, Jun 1-Sep 16, 30% of year
Off-Season, Jan 1-May 31, Sep 17-Dec 31, 70% of year
4.2.2 Marginal plus added fixed costs in bid price
A plant may increase the prices it receives in several ways. First, the owners could bid a
higher price into the spot (and day-ahead) markets. If accepted, these prices will give higher
revenues. The danger is that other plants will then undercut the price bid. Consequently, the plant
may not be called on to run as often and lose revenue. If a plant or set of plants has sufficient
market power, then they may be able to raise their prices without being significantly undercut by
other generation sources.
With new plants, either gas CC or CT, setting the marginal prices 75% of the time, it is clear
that they would need to incorporate their fixed costs into their bids in some way. To simulate an
added bid factor, we added 25% of their fixed costs to the variable cost of each plant in their bid
price. In order to convert fixed costs to variable we used the capacity factor for each plant from
the scenario when they only bid their variable cost. As a consequence, newer plants, with higher
Oklahoma Restructuring Impact 15
fixed capital costs, had higher increases in their bids than the older plants. This slightly changed
the loading order and consequent capacity factors that each plant actually had.
The value of 25% of fixed cost was determined because at that amount, the new CC plants
earned close to 14% on their equity (Table 9). New gas CT’s still make somewhat less than their
expected return, but this analysis does not include extra revenue from ancillary services such as
spinning reserve. These extra services, which are most often provided by CT’s, could raise the
total ROE. Older plants, especially coal and hydro, make large returns on their equity. (Hydro as
modeled has essentially no equity.)
Table 9: Operating and financial results with all plants at market rates and bids include
25% of fixed costs
Plant Type
Capa-city
MW
Capa-city
Factor
%
Time
on
Margin
%
Price
Receiveda
¢/kWh
Marginal
Costb
¢/kWh
Total
Costc
¢/kWh
ROE
%
Regu-lated
ROE
%
Coal 5156 76 0 3.41 0.97 1.45 652.8 11.9
Hydro 1035 29 0 4.69 0.39 1.54 1260730 0.0
Oil 39 17 0 5.30 2.71 4.62 398.9 11.0
Gas ST 4625 7 20 8.08 3.71 5.40 213.1 11.0
Gas CC/Old 909 37 12 4.40 2.98 3.48 255.5 12.9
Gas CT/Old 343 6 1 8.47 3.45 7.64 51.7 11.0
Gas CC/New 8309 79 54 3.62 2.31 3.63 13.9 14.0
Gas CT/New 1674 15 12 6.45 2.91 7.79 6.7 13.9
a Price received is total revenue divided by sales.
b Marginal cost is variable and start-up costs of operation plus 25% of fixed cost
c Total cost includes operations, depreciation, interest, taxes, and expected return on equity
The older gas plants also make very good returns, partly from the rise in price from the new
CT’s, and partly from the increase in bid prices from plants operating for only a small part of the
year. For example, adding 25% of the fixed cost of $15/kW-year to a plant that operates 1% of
the year, or 88 hours, increases its bid price by 4.3 ¢/kWh . If the plant runs 10% of the year the
added part is only 0.43 ¢/kWh; if it runs 0.1%, or 9 hours, the added amount is 43 ¢/kWh. This
impacts not only its own profitability but the prices of all plants running at that time.
Figure 5 shows the real-time prices over the year under this scenario. Prices are slightly higher
in the off-season and during the low-demand period of the peak season, as compared to Figure 4.
However, prices rise higher and more rapidly in the peak season as the plants with low capacity
factors are called on and their bids include a higher proportion of fixed costs.
However, in this scenario, prices to all customers increased over what they would pay under
regulated rates (Table 10 and Figure 3). So while new plants would be solvent in this scenario,
the price impact on consumers makes this scenario less feasible.
Variations on the percentage of fixed costs added can be run, including having some plants,
such as the older plants, not including the adder. However, as fewer plants include the added cost
Oklahoma Restructuring Impact 16
then they by necessity must include a higher percentage in order to recoup their fixed costs. This
causes them to be called on less often since the plants without the expense in their bid now are
priced lower. The end effect is that new, efficient plants are called on much less frequently than
more expensive, older plants, which is likely not what the reality would be.
Figure 5: Real-time market prices with plants adding 25% of fixed costs to bids
0
2
4
6
8
10
12
14
0 20 40 60 80 100
Percent of Season
¢/kWH
Peak Season, Jun 1-Sep 16, 30% of year
Off-Season, Jan 1-May 31, Sep 17-Dec 31, 70% of
Table 10: Prices with all plants at market rates and bids include 25% of fixed costs
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.84 7.18 4.18 7.52 0.34
Commercial 2.89 3.15 6.04 4.03 6.92 0.88
Industrial 1.00 3.04 4.04 3.98 4.99 0.95
4.2.3 Regulated pricing of existing coal and hydro plants
Among the major beneficiaries of a change to pricing using market-based prices are the
existing low-cost producers, notably coal and hydro facilities. As shown in Table 9, these two
plant types receive prices much higher than average costs, while having no influence on the
market price since they are never on the margin. It might be feasible during restructuring to
mandate that they sell their power at cost plus reasonable profit, instead of at the full market
rates. There are precedents of this in other states. For example, as part of its restructuring,
Oklahoma Restructuring Impact 17
California required that the nuclear and hydro facilities owned by the investor-owned utilities
price their production at cost. While the rest of the production in the state became very expensive
this past year, the nuclear and hydro plants provided some measure of stability.
To examine the impact of having coal and hydro plants sell power at cost instead of market,
we modified ORCED so any or all plants could price at a fixed price. We set the price for coal
and hydro plants so that they would recover their costs and reasonable return on equity. These
plants as modeled actually have very little equity in them by 2010, both because of their age and
because the plants owned by government entities were modeled as being debt-financed so with
essentially no equity per se. As a result, customer prices dropped such that residential consumers
saw prices 0.34 ¢/kWh lower under restructuring than under regulation, and other customer saw
only modest increases (Table 11 and Figure 3). Coal and hydro plants had their average price
drop to their costs and ROE’s of 11.9% and 0% respectively; while all other plants had the same
returns as in Table 9.
Table 11: Prices with market prices including 25% fixed cost but existing coal and hydro
plants priced at costs
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.84 7.18 3.51 6.85 -0.34
Commercial 2.89 3.15 6.04 3.25 6.14 0.10
Industrial 1.00 3.04 4.04 3.19 4.19 0.15
4.2.4 Contracts versus spot-market pricing
An alternative to plants selling on the spot market is for plants to sign long-term contracts for
some or all of their production. The prices may include a fixed cost for the capacity of the plant
and a variable cost for the actual production. This is similar to the system-wide pricing that
occurs under regulated rates, but on a plant level. Total revenue requirements are calculated by
summing the fixed and variable costs of operations, including capital costs such as depreciation,
interest, and a reasonable rate of return. The revenue requirements are then charged to customers
either through a single energy-related price or through separate demand and energy charges.
(This is a simplification of the actual rate-setting process and types of rates created.)
As an example, below is a statement from the 10-K form from Cogentrix on their power
project financing and contracts:
PROJECT AGREEMENTS, FINANCING AND OPERATING
ARRANGEMENTS FOR OUR OPERATING FACILITIES
Project Agreements
Our facilities have long-term power sales agreements to sell electricity to electric
utilities and power marketers. A facility's revenue from a power sales agreement
Oklahoma Restructuring Impact 18
usually consists of two components: variable payments, which vary in accordance
with the amount of energy the facility produces, and fixed payments that are
received in the same amounts whether or not the facility is producing energy.
Variable payments, which are generally intended to cover the costs of actually
generating electricity, such as fuel costs, if supplied by the operating facility, and
variable operation and maintenance expense, are based on a facility's net electrical
output measured in kilowatt hours. Variable payment rates are either scheduled or
indexed to the fuel costs of the electricity purchaser and/or an inflationary index.
Fixed payments, that are intended to compensate us for the costs incurred by the
project subsidiary whether or not it is generating electricity, such as debt service
on the project financing, are more complex and are calculated based on a declared
production capability of a facility. Declared production capability is the electric
generating capability of a plant in megawatts that the project subsidiary
contractually agrees to make available to the electricity purchaser. It is generally
less than 100% of the facility's design production capability dictated by its
equipment and design specifications. Fixed payments are based either on a
facility's net electrical output and paid on a kilowatt-hour basis or on the facility's
declared production capability and can be adjusted if actual production capability
varies significantly from declared production capability. (Cogentrix 2000)
If the long-term contracts are based on the company receiving their expected rate of return,
there is little difference in prices between the regulated market price and contract price. We ran
ORCED with all plants selling under long-term contracts at prices based on their expected
returns. As a result, the restructured prices to customers were the same as the regulated prices,
and all plants made their regulated ROE. If that is the case, the main difference between a
restructured market and regulated market is that the individual plants may contract directly with
end-customers rather than just the local utility or wholesale marketers.
The question then arises on whether a plant would choose to sign long-term contracts or bet
on the spot market for pricing. And, if it were to sign long-term contracts, would they be priced
close to their costs with a reasonable profit, or would they try to set prices close to the expected
average spot price? Would they be willing to sacrifice some profit for the sake of firm prices?
Similarly, how much more are customers willing to pay over the expected spot prices in order to
get some price surety? These questions are asked daily by generators, outside investors,
marketers, and utilities in today’s market. Different business plans and portfolios are developed
in a complex combination of long-term and short-term purchases, generation, and hedging
strategies. Companies may choose to sell some of their generation under long-term contracts,
some on the day-ahead or spot market, some as either a spinning or non-spinning reserve, as well
as save some for internal use if it is a cogeneration project. All these factors influence the final
market.
4.3 Market power: modified bids and withheld capacity
In establishing the base case we modeled that all plants would include a portion of their fixed
costs in their price. This is a simple version of market power in that all suppliers tacitly agree to
Oklahoma Restructuring Impact 19
increase their bids. Two more complex mechanisms for plant owners to exert market power are
to raise their bid prices as a group or to withhold some of their lower cost capacity.
4.3.1 Group bids
In a more complex market scenario, the owners of new combined cycle plants may recognize
that their bids can be raised to just below the cost of the next more expensive technology. The
marginal costs of the new CC plants are around 2.35 ¢/kWh; the next most expensive major plant
type (new Gas CT) have marginal costs around 2.86 ¢/kWh. If the CC plants were to raise their
bids to 2.85 ¢/kWh, they should still have roughly the same sales, yet earn an additional 0.5
¢/kWh when they are the marginal producers.
Table 8 above showed the results if all plants just bid marginal prices. If just the new CC
plants raised their prices to 2.85 ¢/kWh, their ROE does improve from the –2% in the table to
3.4%. However, the higher-cost plant types see little change in their ROE’s since their prices
were no different than before. Coal and hydro facilities receive an extra windfall as the average
price goes up from 2.77 to 3.03 ¢/kWh. Thus, even if all new combined cycle operators, as
shown in Table 4, raised their prices together to just below the gas CT prices, there is only some
improvement in their profitability.
As a further step, we considered if all new CC and CT plants raised the bids together to the
level of the next technology. Gas Steam plants begin entering the market at bid prices of 3.26
¢/kWh. Raising the CC and CT bids to just below this amount allows them to collectively still
operate the same amount while making an additional 0.4 ¢/kWh. CC plants’ ROE rose to 9.4%
and CT plants rose from –4.2 to -3%.
Lastly, what happens if all the new plants bid 3.25 ¢/kWh but one? Suppose that one of the
new CT’s that normally would have operated in a peaker mode with a capacity factor of 25%
chose to bid its marginal cost instead. It then becomes a baseload unit running 86% of the year,
and earning +5% ROE instead of -2%. This is a strong incentive for individual plants to lower
their bids, with the hopes that no one else does.
Plants are very reliant on the existence of higher-priced plants in order to make their profits in
a spot market. Even if a large segment of the capacity raises its price, it risks being undercut by
other technologies unless they bid to just below the others’ marginal cost. The incentive for
individual plants to “cheat” and lower their bids can undermine the market power potential. Only
if a substantial majority of the participants in the market, especially those with higher costs, raise
their bids proportionately, do profits rise for all.
4.3.2 Withholding capacity
The other mechanism by which market power can be exercised is through the withholding of
capacity. If low cost producers choose to not bid a portion of their capacity, then the market-clearing
price will be higher as more expensive plants replace the lost capacity. The individual
plant that does not run will lose money, but the other plants that the producer owns may earn
enough more through higher prices to compensate.
Oklahoma Restructuring Impact 20
We ran two examples of capacity withholding: one of a company that owns multiple new
plants, and one of an existing producer. In the first case, we lowered the production 10% from
the 600 MW Lawton plant owned by Smith Cogeneration. We had modeled it at slightly lower
cost than the Pocola plant, so reducing the Lawton plant by 52 MWyr increased the production
of Lawton by 8 MWyr. Plants owned by other companies supplied the rest of the missing
production. Because the replacement power was more expensive, the average price to customers
increased 0.06 ¢/kWh. Smith’s revenues declined $6 million, but since they did not have the
expense of production, their net income actually rose $2 million. Their overall ROE increased
from 12.7% to 13.2%.
In the other example, we reduced the production from the 1015 MW Sooner coal plant owned
by Oklahoma Gas and Electric by 10%. This led to an increase in production from other plants,
owned by both OG&E and others. OG&E’s ROE increased from 46% to 66%, despite the 5%
lower overall production. Average prices to customers increased 0.12 ¢/kWh as higher-cost
plants provided a larger share of the total. The coal plant’s production declined by 76 MWyr, but
other plants owned by the utility, most notably their peaker gas steam plants, increased
production by 6 MWyr. Since these other plants are unregulated and very profitable in this
scenario, the utility’s overall profitability increased.
A key reason for improvement in the utility’s ROE was the unintended consequence of
regulating the price from one plant but not others. OG&E earned the regulated 11% return on
their coal plant regardless of its actual production. By reducing its output, other plants owned by
OG&E that were not regulated in the scenario increased their production and their revenues
(especially since prices increased as well), while the coal plant returns were not reduced.
Because we are only considering the production from Oklahoma plants as substitutes for the
lost production, in both cases, reducing production had the effect of raising net income. Prices
rose sufficiently to offset the lost income from the production. In the broader regional electricity
market, however, capacity from outside the state may enter the market to make up the lost sales
without causing a significant increase in price. This depends on the cost and supply of extra
generation in the outside market. In the larger regional market, the utilities have less impact on
the overall reserves. Since we modeled a significant amount of sales into the outside market,
withholding capacity may simply lower external sales with no effect on overall prices. Also, if
the plants continued to operate at lower capacity, new construction would enter into the market
to more permanently negate this market power. While we used two specific utilities in these
examples, we do not wish to imply that they and they alone wield market power in the Oklahoma
electricity market.
4.4 Elasticity and real-time pricing
Most experts on restructuring recommend that customers have real-time pricing available
(Taylor and VanDoren 2001, Hirst 2001). The actual cost of generation can vary greatly between
seasons or even hours. When customers are only aware of the average price from the previous
month, they have little knowledge or incentive to adjust their demands as the real-time price
changes. If even a small fraction of customers responds to high prices through lower demands, it
can have a large impact on the overall market. Plants that normally would run only a few hours
Oklahoma Restructuring Impact 21
are called on less often, while other plants see more use as customers increase purchases during
low-cost times. Average prices go down and profitability goes up. Real-time pricing can be
implemented whether or not restructuring occurs, although restructuring facilitates it through
competition and increased opportunities for change.
To explore the potential of real-time pricing, both in a regulated and market environment, we
recalculated the demand load profiles for each retail customer category. Using the real-time
prices shown in Figure 5, we raised or lowered the customer demand depending on how much
the price differed from the average price. We did not modify demands in the Other category,
which includes the wholesale exports and sales internal to Oklahoma that are not in the three
main categories.
We used a price elasticity of –0.10, meaning that a 10% increase in price reduces demand by
1%. There is little information on the correct value to use, although recent price changes in
California give a clue. In San Diego in the early summer of 2000, the local utility was allowed to
raise its prices to the market rates. A study of the impact on demand was conducted by James
Bushnell and Erin Mansur of the Program on Workable Energy Regulation (Bushnell and
Mansur 2001). They found that a doubling of rates resulted in a drop in demand of 2.2 to 7.6
percent. More recently, California had rate increases in the late spring of 2001 and saw a
reduction of demand of 12% comparing June 2001 use to June 2000 use, after taking out
weather-related factors (Hirsh and Kennedy, 2001). However, price changes differed between
customer categories, and a large amount of non-price-related incentives were also put in place.
Neither of these data conclusively provides information on customer response over a long
period. Generally, elasticity increases as customers become more familiar with the prices and
have the time to invest in equipment that will shift or reduce demand. So while San Diegans
responded with an elasticity factor between –0.02 and –0.08, given time they may increase their
responsiveness and raise the factor. A broader study on the impacts of increasing customer
responsiveness is included in our report on generation adequacy (Hirst and Hadley 2000).
Each customer category’s load shape changed slightly in response to the variable prices.
Figure 6 shows the change to the residential customer load shape in the peak and off-peak
season. Total energy purchases were kept the same but electricity use during the highest-priced
part of the peak season was reduced by 7.7% (570 MW) because of the high prices shown in
Figure 5. The Off-peak season saw very little change from the original load duration curve
because prices did not vary greatly. The commercial and industrial sectors saw slightly higher
percentage declines in their peak demands (9.6% and 11.6% respectively). This was mainly
because the price differential was greater for them since the fixed T&D cost component are a
lower proportion of their overall prices.
Oklahoma Restructuring Impact 22
Figure 6: Change in Residential Load Duration Curve from real-time pricing and elasticity
0
1000
2000
3000
4000
5000
6000
7000
8000
0 20 40 60 80 100
Percent of Season
Power Level, MW
Original Residential Peak
New Residential Peak
Original Residiential Off-peak
New Residential Off-peak
The result of changing each customer’s load profile was a reduction in retail peak demand of
1,240 MW. This represents a reduction of 9% in retail peak demands. Since exports and other
sales were not modified, the total drop was 6.1% of the original system peak demand. The
system load factor, which is the ratio of the average demand to the peak demand, improved from
57.4% to 61.1%. This means that while fewer plants may be needed to meet demand, those that
are used run for a longer part of the year.
We first ran ORCED with the new demands but same set of plants. This resulted in a reserve
margin of 15.4%, significantly higher than the 8.4% with the original demands. Customer prices
averaged 0.25¢/kWh lower than in the base scenario, but return on equity for plants dropped. The
peaker plants were most seriously affected. While new CC plants had ROE drop from 13.9% to
9.1%, the new CT plants dropped from 6.7% to 0.2% and the old CT plants dropped from +52%
to –56%.
Because of the lower demand, we removed two new CT’s from the list of plants, reducing
capacity by 620 MW. This still left a larger reserve margin than in the original case, 12.2%
versus 8.4%. Despite the increase in reserve margin, the total reliability of the two systems as
measured by the Loss of Load Probability is roughly the same. The extra reserve margin is
needed to make up for the possibility of other plants not being available when needed. With the
higher load factor there is a greater need for reserves to back up any plants with forced outages.
In addition to dropping the two plants, we lowered the fixed cost adder from 25% to 22% to
leave the CC plants with a 14% ROE. In this scenario, prices to consumers still dropped
Oklahoma Restructuring Impact 23
compared to the case without elasticity, by .09 ¢/kWh for regulated prices and .04 ¢/kWh for
market prices. With fewer plants, the returns on equity to the remaining plants stayed nearly the
same. New CC plants saw returns of 13.8%, new CT’s had 7.7%, and old CT’s had a 48% return.
The response of customers to real-time prices has a modest effect on average prices paid. Its
larger impact is on prices paid at the peak. In the case without elasticity impacts, market prices
were 120 ¢/kWh during the short time (~15 hours) when all plants were at full capacity. In the
case with elasticity this price peaked at 100 ¢/kWh. With elasticity and consequent flatter
demand profile, peak prices do not have to rise as much to lower demand to available capacity.
Oklahoma Restructuring Impact 25
5 Economic Analysis
5.1 Introduction
Electricity is a prominent product in the modern economy and a critical input into many
production processes, including those of households. The turmoil in California’s electric power
markets in late 2000 and early 2001 heightened national attention on this product.
In 1998, the value of private electricity production in Oklahoma was 2.3 percent of the total
value of production in the state. In that year, 40.7 percent of Oklahoma’s electricity generation
was used by residential consumers, 26 percent by commercial establishments, and 27.5 percent
by industrial consumers (EIA 2000c).
For this economic analysis we used the highest priced scenario, with all plants pricing at
market rates including coal and hydro (Table 10). Other scenarios, with prices much closer
between regulated and market-based, should show much less economic impact.
The average simulated rate increases of 5 percent for residential prices, 14 percent for
commercial, and 23 percent for industrial amount to a weighted average price increase of 12
percent. Thus, the economic analysis below examines a roughly 12-percent price increase in a
commodity that accounts for 2.3 percent of state production. Against this aggregate backdrop, it
is not surprising that the electricity rate changes identified with potential deregulation of
Oklahoma’s electric power industry have very small impacts on the overall economy of the state.
Depending on the price-change scenario, employment in the state could fall by three or four one-hundredths
of one percent while other property income could rise by about one-third of one
percent. The differences in impact across the scenarios also are small.
5.2 The Method of Analysis
The impact of changes in the three electricity rates was studied with 528-sector input-output
model of the Oklahoma economy.2 Input-output models trace the flows of expenditures through
the production sectors of an economy. Each production sector purchases produced inputs from
other industrial and commercial sectors (called intermediate inputs, or intermediate demand),
both within the state and outside it; hires labor; pays for the use of capital equipment; and pays
indirect business taxes. The labor receiving wages and salaries from each sector spends the
income they receive on the products of these industrial sectors, as well as on domestic and
foreign imports. These expenditures are called “final demands.” Some of the owners of the
capital equipment used in the industrial and commercial sectors live in the state and also spend
their income on locally produced goods and services. Thus there is a circular flow of income,
from production sectors hiring labor, through the spending of that labor income on locally and
externally produced products, with the demand for each product requiring inputs from many
other production sectors.
2 The IMPLAN model, with 1998 social accounts data for Oklahoma, was used in the analysis: Minnesota
IMPLAN Group, Inc., IMPLAN Professional, Version 2.0; Social Accounting & Impact Analysis Software
(Stillwater, Minn.: Minnesota Implan Group, April 1999).
Oklahoma Restructuring Impact 26
The usual method of studying economic impacts with an input-output model is to alter the
final demand for one or more products. This change injects additional income into the economy
at a particular point. That increase in final demand increases intermediate demands for the
products used to produce that good, and it also puts more wage and capital income into
circulation in the economy as more people and machinery are required to produce the additional
output. These dollars are spent as final demands across the entire array of goods produced in the
economy, and the increases in the final demands for these products increases the intermediate
demands for the other goods required to produce them. The money inserted into the economy
circulates through the production sectors and income-receiving households several times,
causing an expansion in the economy larger than the initial increase in final demand. However,
some of this additional income “leaks out” of the local economy during the spending cycles, as
people purchase goods imported from other states and countries, so that each new round of
spending gets smaller than the previous, as the purchases circulate through the economy.
The electricity price increases studied here have a different structure than the final demand
change usually posited for an input-output model. They are price changes rather than changes in
demands. In fact, in the short run, the quantity of electricity demanded, both by households and
firms, is quite inelastic, so that the final and intermediate demands for electricity change virtually
not at all. Instead, they cost more to produce. To accommodate this change to the input-output
framework, we altered the production structure of each sector, making each sector spend more on
electricity than before, and the set of final demands for households and other institutions (e.g.,
state and local government).
In changing the production structure of the industrial and commercial sectors, we recognized
that Oklahoma is thoroughly integrated into the United States economy, so that any changes in
its local production costs cannot affect the prices it pays for traded commodities.3 Similarly with
the labor and capital markets. Any increase in electricity prices must be offset exactly by
reductions in expenditures on other inputs, both other produced inputs and labor and capital.
Correspondingly in the expenditure patterns of households and institutions, we keep the initial
expenditures constant, taking the extra expenditures on electricity out of all other purchases, in
proportion to their expenditure shares in the budget. This imposes very nearly a zero elasticity of
demand for electricity on households.
In the first round of effects from the increase in electricity prices, industrial and commercial
producers reduce their intermediate demands for virtually all other inputs besides electricity, and
households and other institutions reduce their purchases of all products other than electricity. In
the next round, come reductions in intermediate purchases by all sectors other than electricity, in
3 Many services are nontraded, and their local prices could diverge somewhat from the prices of similar
products in other locations. However, national labor and capital markets, and national markets for the purchased
inputs of those activities, act to keep those prices rather close together across locations. The only input that
ultimately cannot be moved around to keep its price equalized across locations is land, and land rents will rise or fall
locally to absorb differences in prices of nontradable goods and services. An alternative approach to modeling the
production changes in each sector would have been to take the incremental electricity expenditure one hundred
percent out of the value-added category “other property income,” which includes land rents. Our method supposes
that producers manage to reduce expenditures proportionally across all their other inputs, rather than experiencing
the entire cost squeeze in the rents (residual profits) to land they use (Roback 1982).
Oklahoma Restructuring Impact 27
response to the direct retrenchments in employment and intermediate purchases. These are
indirect effects of the initial round of cuts. However, payments to the electricity sector increase
in the direct impact, and that sector’s demands for intermediate inputs and for labor and capital
rise, at least partially offsetting reductions elsewhere in the economy. The expanding electricity
sector, as well as expansions in several closely related industries, coal in particular, increases
labor income, which gets spent on the full array of goods and services in the economy. With
some sectors expanding and others contracting, the changes in income going to employees
induce a series of corresponding expansions and contractions in expenditures across all the
sectors.
In an input-output model, a state’s exports would change only in response to outsiders’
demands for them, although imports respond to both intermediate and final demand changes
originating within the state. Thus exports do not change simply because a state “can produce
more” of some good. Outside demanders must ask for more of specific goods the state produces.
We have not considered an increase in outside demands for electric power from Oklahoma
generators. A broader, regional study on the economic consequences of power production and
prices may be worthwhile in the future.
5.3 Numerical Results
We present impact results for three scenarios of electricity price changes. In the first scenario
(all plants, including hydro- and coal-generation, at market prices, including an adder 25 percent
of avoided cost), the residential rate rises by 5 percent, the commercial by 15 percent, and the
industrial by 23 percent (Table 12). The second scenario is similar but with the hydro plants
regulated. It has the residential rate rise by 4 percent, the commercial by 13 percent, and the
industrial by 21 percent. In the third scenario (all plants, including hydro- and coal-generation, at
market prices, customer demand shape changed due to elasticity), residential rate rises by 5
percent, the commercial by 16 percent, and the industrial by 26 percent. To gain some insight
into the contributions of these separate rate changes within any scenario, we apply the rate
changes separately for the third scenario, and find that in some cases the rate increases for
different customer classes have opposite effects in some sectors.
Table 12: Percent increase in prices market-based versus regulated, with most or all plants
at market rates
All plants (w/ coal and
hydro) at market prices
All plants but hydro at
market prices
All plants at market,
elasticity change demand
Residential 5 4 5
Commercial 15 13 16
Industrial 23 21 26
Table 13 and Table 14 report the output changes for individual industrial and commercial
sectors that are particularly strongly affected–although in absolute terms, all of the changes are
small, with the exception of those in the private electricity sector. Table 13 identifies the sectors
experiencing the strongest contractionary impacts, and Table 14 reports the comparable
information for the sectors undergoing the strongest expansions. The third scenario, which has
Oklahoma Restructuring Impact 28
the highest weighted-average rate change, has the strongest impacts, both negative and positive.
The first scenario, with the lowest weighted-average rate increase, has the smallest impacts
among contracting industries (Table 13), but generally has the second-largest impacts, after
scenario 3, among expanding industries (Table 14).
Table 13: Industrial sectors with output decreases, percents
industry
Scenario
1
% change
Scenario
2
% change
Scenario
3
% change
Scenario 3
Industrial
% change
Scenario 3
commercial
% change
Scenario 3
residential
% change
Explosives -0.595 -0.551 -0.679 -0.764 0.045 0.040
Logging camps &
logging contractors
-0.516 -0.474 -0.586 -0.615 0.008 0.020
Uranium, radium,
vanadium ores
-0.411 -0.376 -0.465 -0.468 0.001 0.002
Metal mining
services
-0.411 -0.376 -0.465 -0.468 0.001 0.002
Agriculture, forestry,
fishery services
-0.393 -0.357 -0.441 -0.416 -0.009 -0.016
Plastics, materials &
resins
-0.277 -0.253 -0.312 -0.311 -0.001 -0.000
Synthetic rubber -0.231 -0.210 -0.260 -0.258 -0.002 -0.000
Paperboard
containers & boxes
-0.216 -0.195 -0.241 -0.201 -0.030 -0.010
Wood pallets &
skids
-0.200 -0.181 -0.224 -0.184 -0.039 -0.001
State & local
government
education
-0.211 -0.180 -0.221 0.092 -0.341 0.028
Animal & marine fats
& oils
-0.180 -0.164 -0.203 -0.194 -0.004 -0.005
At the level of the individual industrial sectors, the change in the industrial rate tends to have
the strongest impact on contracting industries, as shown in Table 13, with the exception of the
state and local government production sector, which experiences the strongest contractionary
impact from the increase in the commercial rate. The commercial and residential rates have very
small impacts on these contracting sectors. Among the expanding sectors, shown in Table 14, the
increase in the industrial rate still tends to have the largest impact on the expansions, but the
magnitudes of the commercial and residential rate increases are much closer to those of the
industrial rate. The 10 to 11 percent expansion in the private electricity sector is in value terms,
not in terms of megawatt hours generated.
Oklahoma Restructuring Impact 29
Table 14: Industrial sectors with output increases, percents
industry Scenario
1
% change
Scenario
2
% change
Scenario
3
% change
Scenario 3
Industrial
% change
Scenario 3
commercial
% change
Scenario 3
residential
% change
maintenance &
repair, other
facilities
0.414 0.360 0.446 0.242 0.065 0.140
railroads & related
services
0.393 0.393 0.487 0.132 0.207 0.147
steam engines &
turbines
1.16 1.011 1.252 0.551 0.411 0.290
coal mining 1.71 1.489 1.844 0.785 0.624 0.435
federal electric
utilities
10.674 9.286 11.495 5.085 3.777 2.631
electric services 10.704 9.312 11.526 5.099 3.787 2.638
state & local electric
utilities
10.733 9.338 11.558 5.113 3.798 2.645
Aggregate impacts on categories of income are reported in Table 15. The separate
contributions of the different rate classes are somewhat different at the aggregate level than
among the outputs of the sectors reported individually. Employee compensation falls (which,
with a fixed wage, amounts to a reduction in employment) by a very small extent in all three
scenarios, but the driving force behind that reduction is the increase in the commercial rate. The
industrial and residential rate increases actually have minuscule positive effects on employment.
Household consumption increases by small amounts for all income groups. Lower income
groups experience slightly larger increases, with the exception of the highest income group,
which experiences a larger consumption increase than all but the two lowest income groups.
Increases in all three customer classes act to elevate consumption of the lowest three income
groups, but the increase in the commercial rate depresses consumption in households earning
$15,000 per year and above. Nevertheless, these are all very small changes and may be driven
primarily by how the IMPLAN model allocates wage and property income to households.
Claiming that these rate increases disproportionately affect different income groups on the basis
of this analysis would be exaggerated.
Proprietary income4 and other property income5 both rise, the former by about one tenth of
one percent, the latter by about one third to four-tenths of one percent. Indirect business taxes
also rise, by about the same percent as other property income. The relative contributions of
changes in the residential, commercial and industrial rates differ across these income groups.
State and local government non-educational activities increase by a considerable amount,
compared to the typical impacts of these rate changes. They respond positively to all three
categories of rate increase, while educational and investment respond negatively to the
4 Income to self-employed individuals, typically private business owners, doctors, lawyers, etc.
5 Income from interest, rents, royalties, dividends, and profits, including rents paid to individuals on property
and corporate profits earned by corporations.
Oklahoma Restructuring Impact 30
commercial rate increases, which reduces their overall sensitivity to the rate changes to virtually
nil. Capital investment also increases slightly in response to all the rate changes. It responds
positively to rate changes in all three customer classes, but somewhat more strongly to changes
in the industrial rates, probably reflecting increased demand for capital equipment in private
electricity generation and coal.
Table 15: Economic impacts of changes in electricity price schedules, percent change
Aggregate income
category
Scenari
o 1
%
Scenario
2
%
Scenario
3
%
Scenario 3
Industrial
%
Scenario 3
commercial
%
Scenario 3
residential
%
Employee
compensation
-0.039 -0.034 -0.042 0.005 -0.059 0.011
Proprietary income 0.102 0.086 0.106 0.026 0.032 0.049
Other property income 0.385 0.334 0.413 0.183 0.124 0.107
Indirect business taxes 0.329 0.287 0.355 0.178 0.087 0.0900
Household < $5k 0.128 0.112 0.139 0.069 0.035 0.035
Household $5-10k 0.084 0.073 0.090 0.047 0.019 0.024
Household $10-15k 0.064 0.056 0.069 0.040 0.006 0.023
Household $15-20k 0.056 0.048 0.060 0.041 -0.005 0.024
Household $20-30k 0.043 0.037 0.046 0.037 -0.015 0.024
Household $30-40k 0.040 0.035 0.043 0.036 -0.018 0.026
Household $40-50k 0.030 0.026 0.033 0.031 -0.023 0.024
Household $50-70k 0.024 0.021 0.026 0.028 -0.026 0.024
Household $70k+ 0.069 0.060 0.074 0.044 -0.003 0.034
State & local govern-ment,
non-education
0.144 0.127 0.157 0.092 0.037 0.028
State & local govern-ment,
education
-0.000 0.001 0.003 0.092 -0.117 0.028
State & local govern-ment,
investment
-0.000 0.001 0.003 0.092 -0.117 0.028
Capital investment 0.153 0.133 0.165 0.079 0.046 0.040
5.4 Conclusions of input/output analysis
The aggregate economic impacts of the electricity rate increases that appear likely to emerge
from deregulation as projected here are very small. These impact projections are likely to be on
the high side of actual, long-run impacts, since the assumptions of the input-output framework,
as well as assumptions we adopted for this study, minimize the opportunities to substitute away
from electricity in both final and intermediate demands. We did not attempt to simulate the
potential for substitution away from electricity into natural gas for some energy uses, but over a
five- to ten-year period, if some classes of rates stayed twenty to twenty-five percent higher,
some substitutions surely would occur in specific uses such as heating, air conditioning and
water heating. Additionally, the assumption made here to take the incremental electricity cost out
of all inputs instead of sinking them all into land rents, would tend to elevate the short-run
response to the rate increases through the indirect effects on demands for other intermediate
products.
Oklahoma Restructuring Impact 31
6 Results and Conclusion
Based on the analysis above, there are several important results for decision-makers in
Oklahoma. First, due to economics and transmission constraints, it is likely that some of the
proposed new plants will be cancelled or postponed. Second, existing low-cost coal and hydro
capacity will make high returns if allowed to price their production at the wholesale market rates,
at the expense of consumers. Based on the rationale of stranded costs as applied by FERC and
other states undergoing restructuring, it may be advisable to continue to have their production
priced at their cost including a reasonable return instead. Care must be taken to avoid market
manipulation if companies own both regulated and unregulated plants. Third, the economics of a
spot market for electricity pricing do not favor new plants with high capital costs, unless they can
incorporate some of their fixed costs into their bids and have sufficient market power to avoid
being tremendously undercut by competitors. Fourth, customer response to real-time prices can
serve to lower peak demands significantly. Less new construction is needed and prices are
reduced modestly. Fifth, even using the highest electricity price increases we modeled, the
overall economic effect on the state’s economy was slight. Employment decreased less than
0.05% overall and showed increases in some sectors, notably mining and the electric industry
itself.
6.1 Excess capacity and growth in exports
As described in Section 3, the amount of generating capacity planned for construction in
Oklahoma greatly exceeds the growth in demand. Even by 2010, internal demand is only
expected to rise around 26%, while in-state capacity is projected to double by 2004. While
expansion of power exports may consume some of this excess, it would have to expand from
current 1,000 MW to over 9,000 MW to utilize all of the capacity and leave a reasonable reserve
margin. Transmission capacity limits are likely to limit this export to 6,000 MW at the most,
assuming that other states have a need for this power.
In fact, there is a growing realization that the market may be set for a bust in the near future.
According to Christopher Ellinghaus, an investment banker at Williams Capital Group, power
companies across the country have proposed 350,000 MW of new plants by 2004, but only
100,000 MW of this is expected to actually be built (Bannerjee, 2001). According to the New
York Times article, transmission constraints and power plant economics are both playing a role
in the lowering of expectations. Many of the announcements of new capacity were based on the
expectation of broadly rising prices, as exemplified by California and the entire western region.
With the recent decline in wholesale prices, new plant economics are not as favorable.
Furthermore, many of the plants are being located in states with large gas resources, such as
Oklahoma and Louisiana. However, transmission systems are not being upgraded quickly
enough to be able to ship this excess capacity to states needing it. In Oklahoma, only one
additional 345 kV line is planned between now and 2010. Expansion of the transmission system
is more difficult to construct than new generation. Current transmission owners see little benefit
to build since it dilutes the value of their existing lines. The same goes for owners of plants in the
high-cost region. Landowners do not see the benefit since the power is to be used by others.
Even intervening states frequently object to new lines. For example, Connecticut recently vetoed
Oklahoma Restructuring Impact 32
a proposed transmission line to Long Island since they would not benefit from it and it may
disturb some oyster beds in the region (Behr 2001).
6.2 Regulation of Coal and Hydro
Existing coal and hydro plants have the good fortune of having low costs, both operating and
capital. They are and will remain a significant fraction of the overall capacity in the state (28% in
2010) but not enough that they become the marginal producers and consequent price setters. The
average price paid to coal plants over the year is 3.41 ¢/kWh while the coal and hydro average
costs are only 1.5 ¢/kWh (Table 9). Hydro plants are preferentially run during peak times so see
an even higher average price. If the plants receive these market prices, customers’ average prices
are higher by 0.74 ¢/kWh than if the plants received cost-based rates.
When some other states have restructured they provided that some power plants would
continue to be priced at their costs rather than sell at the market rates. The original rationale
behind the cost-based pricing was that it was thought that the utilities had some plants and
contracts with overall costs much higher than the market would be and deserved to recoup these
costs. An implied social contract existed in the past that utilities would be guaranteed a
reasonable return on prudent investments. If certain historical investments could not be
recovered in a restructured market, then they should be recouped through a “stranded cost” fee or
transition charge. Most states that have restructured implemented stranded cost recovery for their
utilities. The amount of recovery was set at the start of restructuring, with later true-ups as costs
became better known (Hirst and Hadley 1998).
Oklahoma is faced with the opposite situation of other states; its power plants, especially the
coal and hydro plants, have costs much lower than the market prices. It may be advisable that
this difference be returned to customers in some fashion, through cost-based pricing (such as
modeled here), through rebates following the sale of plants, or other mechanisms. However, the
mechanisms for these plants to continue selling at their cost rather than market must be carefully
constructed to avoid unintended consequences, misplaced incentives, or market manipulation.
6.3 Impact of Market Power
In a purely competitive market where supply bids into a market until demand is satisfied, the
optimum bid for any supplier is to bid at their marginal cost. Prices then are set by the highest
price bid that fulfills demand. This was described in more detail in our Phase I report. The
problem for the electric industry is that in an industry with a high ratio of fixed to variable costs,
there is a greater likelihood that the resulting prices will not cover their fixed costs, leading to
boom and bust cycles. Examples include such industries as airlines, steel, and cement. In many
such industries, what happens is a build-up of inventories that leads to temporary plant closures
as demand and supply equilibrate. The lack of an economical electrical storage mechanism
makes this process more problematic for the electric generation business. The inelasticity of
supply and demand can lead to great volatility.
This problem is especially acute if a large fraction of the suppliers has similar marginal costs.
If one supplier tries to include fixed costs in its bid at any given time, another supplier can price
slightly below this (but still above their marginal cost) and take the sale. With many suppliers,
Oklahoma Restructuring Impact 33
this rationale drives the price down to the plants’ marginal costs and none of them recover their
fixed costs.
One definition of market power is the ability to price goods above the competitive level and
make those prices stick. This can only happen if a supplier or group of suppliers have a large
enough share of the market and that customers do not have a ready substitute for the product.
In our scenarios, we showed how if all suppliers price at their marginal costs, then the new CC
and CT plants lose money. We then used a simple model of market power where all suppliers
incorporated 25% of their fixed costs (including capital costs) in their bid prices. This simple
formula provided most new plants with sufficient income to justify their construction, while
providing many older plants with large profits. A more complex and realistic form of market
power was modeled by assuming that some low cost technologies raised their prices to just
below the level of the next major technology, but others just priced on their margin. While this
improved the revenue for these low-cost technologies, it was not sufficient for them to fully
cover their fixed costs. They had to have the more expensive technologies also raise their bids so
that all would gain revenue, at the expense of consumers. Furthermore, if any one plant within
the technology grouping broke ranks and lowered their bid, then they earned much more
revenue. This argues against a strong amount of market power through pricing strategies.
The other form of market power we considered was the ability of one company to withhold
capacity from its lower cost plants in order to increase the prices and net revenue received by its
remaining plants. This form of market power proved more successful in our examples. When the
owner of two CC plants lowered the operation of one plant by 10%, the sales increased slightly
for the other. But more important, the prices that all plants received increased such that the
owner of the two plants had higher profits. For the company that owned both regulated and
unregulated plants, withholding capacity proved even more successful. The company earned
more on its unregulated plants through increased sales and prices, and the coal plant continued to
earn its regulated return even though it produced 10% less.
Our modeling overstates the market power influence of the plants because we only model the
plants within Oklahoma. In reality, power plants from other states may offset the lost production
of these plants, so that prices would not rise as much. The influence of these other plants would
require a broader regional study of the power system.
6.4 Impact of Price Elasticity
Customer response to real-time prices can lower the peak demands significantly, by 8% or
more. This lowers the amount of capacity needed to meet demands within Oklahoma. This can
free up the capacity for external sales (if transmission capacity exists) or lessens the need for new
plants. It has a small effect on the average prices paid, because the largest impact is on the small
part of the year when demand is highest. If customers simply shift their demands to other times,
then total sales are not affected. Suppliers may want to adjust their pricing to reflect the change
in plant utilization, lowering the amount they need to raise their bidding to recover fixed costs.
Oklahoma Restructuring Impact 34
6.5 Economic impact on state
The aggregate economic impacts of the range of electricity rate increases derived in this study
would be very small. Depending on the rate scenario, aggregate employment in the state would
fall by as much as 0.042 percent or by as little as 0.034 percent. Neither change would be
detectable in routine employment statistics. Outputs in industrial and commercial sectors not
intuitively related intimately to the electricity sector are affected by correspondingly small
percentages—by roughly one-half of one percent either up or down. The output of coal mining
increases by 1.7 to 1.8 percent, depending on the rate scenario, while the value of the private
electricity sector’s output increases by 9 to 11.5 percent, which is roughly the weighted-average
price change of unchanged megawatts of generation. Alternative assumptions used in the
economic analysis probably would yield even smaller impacts.
6.6 Conclusions
The economic impact of restructuring the electric power industry could be relatively modest
or could raise prices to consumers. A key difference will be how the restructuring takes place:
what plants are included in restructuring, how costs or prices are communicated to consumers,
and whether capacity additions are in line with expected growth in demands.
Any restructuring must take into account that many of the existing plants have costs well
below market rates. The difference between cost and market prices are currently received by
consumers since the plants’ production is priced at cost plus a reasonable return. Policy-makers
will need to address how this future price and cost difference is shared between the state’s
consumers and the owners of the facilities.
It appears that the announced new plants to be constructed in the state are well in excess of the
internal needs of the state and more than the transmission system can effectively export. Delays
or cancellations are likely in order to prevent a glut on the market. Customer response to real-time
prices and competition in external markets could further reduce the need for new plants.
Information such as this study, and evaluation of the market by developers and the OCC, should
help to avoid the worst of any market volatility due to an imbalance between supply and demand.
Oklahoma Restructuring Impact 35
7 References
Bannerjee, Neela, 2001, “As Prices Fall, Utilities Weigh the Economics Of New Plants”. New
York Times, Section C, p. 1, August 22.
Behr, Peter, “For Operators, a Daily High-Wire Act”, The Washington Post, August 22, p. A01.
http://www.washingtonpost.com/wp-dyn/articles/A42384-2001Aug21.html
Bushnell, James, and Erin Mansur, 2001, The Impact of Retail Rate Deregulation on Electricity
Consumption in San Diego, PWP-082, Program on Workable Energy Regulation (POWER),
University of California Energy Institute, April.
http://www.ucei.berkeley.edu/ucei/PDF/pwp082.pdf
Cogentrix Energy Inc., 2001, Form 10-K405 for 12/31/00, Finnegan O'Malley & Company Inc.,
April.
http://www.secinfo.com/dsVsf.44yt.htm
EIA (Energy Information Administration) 2000, Annual Energy Outlook 2001 with Projections
to 2020, DOE/EIA-0383(2001), U.S. Department of Energy, Washington, D.C. December.
http://www.eia.doe.gov/oiaf/aeo/index.html
EIA 2000a, Electric Power Annual 1999, DOE/EIA-0348(99)/1, U.S. Department of Energy,
Washington, D.C. August.
http://www.eia.doe.gov/cneaf/electricity/epav1/epav1_sum.html
EIA 2000c, State Electricity Profiles, June 2000, DOE/EIA-0629 Washington, D.C, June, 222.
EPRI 1982, Transmission Line Reference Book: 345 kV and Above, Electric Power Research
Institute, Palo Alto, CA.
Hadley, S. W., C. R. Hudson and D. W. Jones, The Potential Economic Impact of Electricity
Restructuring in the State of Oklahoma: Phase I Report, ORNL/CON-482, Oak Ridge National
Laboratory, March 2001.
http://www.ornl.gov/ORNL/BTC/Restructuring/OKRestructure2.pdf
Hirsch, Jerry, and Sam Kennedy, 2001, “High Power Prices Lit Fire Under Conservation;
Energy Economists say consumers' quick reaction shows that earlier rate hikes might have
tempered the crisis”, Los Angeles Times, Part A; Part 1; Page 1; Metro Desk, July 14.
Hirst, Eric and Stan Hadley 1999, Maintaining Generation Adequacy in a Restructuring U.S.
Electricity Industry, ORNL/CON-472, Oak Ridge National Laboratory, Oak Ridge, TN, October.
http://www.ornl.gov/ORNL/BTC/Restructuring/C472.pdf
Hirst, Eric and Stan Hadley, 1998, Transition-Cost Recovery and Trueup Mechanisms,
ORNL/CON–456, Oak Ridge National Laboratory, March.
http://www.ornl.gov/ORNL/BTC/Restructuring/c456.pdf
Oklahoma Restructuring Impact 36
Hirst, Eric 2001, The California Electricity Crisis: Lessons for Other States, Edison Electric
Institute, Washington, DC, July.
http://www.EHirst.com/PDF/HirstCALessons.pdf
Hirst, Eric and Brendan Kirby, 2001 “Transmission Planning: Weighing Effects on Congestion
Costs”, Public Utilities Fortnightly, Vol. 139(14), 56-63, July 15.
Interlaboratory Working Group, 2000, Scenarios for a Clean Energy Future (Oak Ridge, TN;
Oak Ridge National Laboratory and Berkeley, CA; Lawrence Berkeley National Laboratory),
ORNL/CON-476 and LBNL-44029, November.
http://www.ornl.gov/ORNL/Energy_Eff/CEF.htm
Lasseter, Dawson, 2001, “Fuel Usage of New Power Plants”, personal communication,
Oklahoma Department of Environmental Quality, July.
Lawton City Council, 2001, Minutes of Special Called Meeting, Lawton, OK, January 18.
http://www.cityof.lawton.ok.us/citycode/LawtonCity/City_Council_Meeting_Minutes/Year_200
1/1/18.html
Minnesota IMPLAN Group, Inc., IMPLAN Professional, Version 2.0; Social Accounting &
Impact Analysis Software (Stillwater, Minn.: Minnesota Implan Group, April 1999).
NERC (North American Electric Reliability Council) 1998, EGADS: Electronic Generating
Availability Data System, North American Electric Reliability Council, Princeton, NJ.
http://www.nerc.com/~filez/gar.html
ftp://www.nerc.com/pub/sys/all_updl/gads/gar/gar1998.exe
RDI (Resource Data International) 2001a, Powerdat Database, Resource Data International,
Boulder, Colo.
RDI (Resource Data International) 2001b, NewGen Database, Resource Data International,
Boulder, Colo.
Roback, Jennifer 1982,"Wages, Rents, and the Quality of Life," Journal of Political Economy,
90, 1257-1279.
SPP (Southwest Power Pool) 2001, Map of the SPP Region, Southwest Power Pool, Inc.,
January.
http://www.spp.org/Publications/Final_Map_Update_condition.pdf
Taylor, Jerry, and Peter VanDoren, California’s Electricity Crisis: What’s Going On, Who’s to
Blame, and What to Do, Policy Analysis No. 406, Cato Institute, Washington DC, July 3.
INTERNAL DISTRIBUTION
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ORNL/CON-485
ENGINEERING SCIENCE AND TECHNOLOGY DIVISION
THE POTENTIAL ECONOMIC IMPACT OF ELECTRICITY
RESTRUCTURING IN THE STATE OF OKLAHOMA
PHASE II REPORT
S. W. Hadley
C. R. Hudson
D. W. Jones
D. P. Vogt
October 2001
Sponsored by
The Oklahoma Corporation Commission
P.O. Box 52000-2000
Oklahoma City, OK 73152-2000
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
managed by
UT-BATTELLE, LLC
for the
U.S. DEPARTMENT OF ENERGY
under contract No. DE-AC05-00OR22725
Oklahoma Restructuring Impact iii
CONTENTS
EXECUTIVE SUMMARY.......................................................................................................v
LIST OF FIGURES AND TABLES........................................................................................ix
ACRONYMS ...........................................................................................................................xi
1 Introduction ......................................................................................................................1
2 Background .......................................................................................................................3
2.1 Recap of Phase I Analysis ...........................................................................................3
2.2 Rise of Merchant Power ..............................................................................................3
3 Oklahoma Market Data for 2010 .....................................................................................5
3.1 Demand Growth within Oklahoma ..............................................................................5
3.2 Export of Power and Transmission Capacity ...............................................................5
3.3 Generation Supply.......................................................................................................6
4 ORCED Analysis............................................................................................................. 11
4.1 Determining Production scenario...............................................................................11
4.2 Determining price scenarios ......................................................................................11
4.2.1 All plants at marginal-cost based market price ..................................................12
4.2.2 Marginal plus added fixed costs in bid price ...................................................... 14
4.2.3 Regulated pricing of existing coal and hydro plants...........................................16
4.2.4 Contracts versus spot-market pricing.................................................................17
4.3 Market power: modified bids and withheld capacity ..................................................18
4.3.1 Group bids......................................................................................................... 19
4.3.2 Withholding capacity .........................................................................................19
4.4 Elasticity and real-time pricing ..................................................................................20
5 Economic Analysis .......................................................................................................... 25
5.1 Introduction...............................................................................................................25
5.2 The Method of Analysis ............................................................................................25
5.3 Numerical Results ..................................................................................................... 27
5.4 Conclusions of input/output analysis ......................................................................... 30
6 Results and Conclusion...................................................................................................31
6.1 Excess capacity and growth in exports....................................................................... 31
6.2 Regulation of Coal and Hydro ...................................................................................32
6.3 Impact of Market Power............................................................................................32
6.4 Impact of Price Elasticity ..........................................................................................33
6.5 Economic impact on state..........................................................................................34
6.6 Conclusions............................................................................................................... 34
7 References .......................................................................................................................35
Oklahoma Restructuring Impact v
EXECUTIVE SUMMARY
Because of the recent experiences of several states undergoing restructuring (e.g., higher
prices, greater volatility, lower reliability), concerns have been raised in states currently
considering restructuring as to whether their systems are equally vulnerable. Factors such as
local generation costs, transmission constraints, market concentration, and market design can all
play a role in the success or failure of the market. These factors along with the mix of generation
capacity supplying the state will influence the relative prices paid by consumers.
The purpose of this project is to provide a model and process to evaluate the potential price
and economic impacts of restructuring the Oklahoma electric industry. The Phase I report
concentrated on providing an analysis of the Oklahoma system in the near-term, using only
present generation resources and customer demands. This Phase II study analyzed the Oklahoma
power market in 2010, incorporating the potential of new generation resources and customer
responses.
Five key findings of this Phase II were made:
• Projected expansion in generating capacity exceeds by over 3,000 MW the demands within
the state plus the amount that could be exported with the current transmission system.
• Even with reduced new plant construction, most new plants could lose money (although
residential consumers would see lower rates) unless they have sufficient market power to raise
their prices without losing significant market share (Figure S-1).
• If new plants can raise prices to stay profitable, existing low-cost coal and hydro plants will
have very high profits. Average prices to customers could be 5% to 25% higher than regulated
rates (Figure S-1). If the coal and hydro plants are priced at cost-based rates (through long-term
contracts or continued regulation) while all other plants use market-based rates then
prices are lower.
• Customer response to real-time prices can lower the peak capacity requirements by around
9%, lowering the need for new capacity and reduce prices during the peak demand.
• Changes to electric prices on the order of 5% to 20% will have only a modest effect on overall
economic activity within the state.
SUPPLY AND DEMAND
The total of existing and proposed capacity equals 25,690 MW, with a large fraction of that
being new combined cycle plants to be built in the next four years or so (Table S-1). Consumer
demands within the state are projected to grow 26% by 2010, totaling 14,340 MW. Simple
expansion of current exports would set their peak at 990 MW. Total exports could increase
greatly, limited by the maximum capacity of transmission lines exiting the state at approximately
6,050 MW. This would give a total peak demand of 20,390 MW. Dividing capacity by peak
Oklahoma Restructuring Impact vi
demand gives a reserve margin if all plants are built of 26% in 2010 and even higher in earlier
years. While some reserve is required for reliability reasons, such a high level of excess capacity
is not sustainable in a restructured market.
Table S-1: Projected electricity supply and demand for Oklahoma in 2010.
Supply, MW Demand, MW
Existing Plants 12,170 Residential 7,510
New Combined Cycle 11,850 Commercial 3,350
New Combustion Turbine 1,670 Industrial 2,910
Other 570
Max exports 6,050
Total 25,690 Total 20,390
In fact, there is a growing realization that the market may be set for a bust in the near future.
According to Christopher Ellinghaus, an investment banker at Williams Capital Group, power
companies across the country have proposed 350,000 MW of new plants by 2004, but only
100,000 MW of this is actually expected to be built (Bannerjee, 2001). According to the New
York Times article, transmission constraints and power plant economics are both playing a role
in the lowering of expectations. Many of the announcements of new capacity were based on the
expectation of broadly rising prices, as exemplified by California and the entire western region.
With the recent decline in wholesale prices, new plant economics are not as favorable.
Furthermore, many of the plants are being located in states with large gas resources, such as
Oklahoma, Texas, and Louisiana. However, transmission systems are not being upgraded
quickly enough to be able to ship this excess capacity to states needing it. In Oklahoma, only one
additional 345 kV line is planned between now and 2010. Expansion of the transmission system
is more difficult to construct than new generation. Current transmission owners see little benefit
to build since it dilutes the value of their existing lines and/or regulated returns are low. Owners
of plants in high-cost regions may also prefer constraints that keep low-cost power out.
Landowners do not see the benefit since the power is to be used by others far away. Even
intervening states frequently object to new lines. For example, Connecticut recently vetoed a
needed transmission line to Long Island (Behr 2001).
MARKET PRICING AND PLANT PROFITABILITY
In a purely competitive market where supply bids into a market until demand is satisfied, the
optimum bid for any supplier is to price their product at its marginal cost. Prices then are set by
the highest price bid that fulfills demand. The problem for the electric industry is that in an
industry with a high ratio of fixed to variable costs, there is a greater likelihood that the resulting
prices will not cover their fixed costs, leading to boom and bust cycles. Examples include such
industries as airlines, steel, and cement. In many such industries, what happens is a shortage that
boosts profits, leading to a build-up of capacity that then leads to temporary cutbacks as demand
and supply constantly equilibrate. The lack of an economical electrical storage mechanism and
the large sizes of plants makes this process potentially even more of a problem for the electric
generation business. The inelasticity of supply and demand can lead to great volatility.
Oklahoma Restructuring Impact vii
Average prices for the modeled Oklahoma market under regulated rates and with all plants
pricing at the market are shown in Figure S-1. However, in this scenario most new plants lose
money. If plants could raise prices by adding some of their fixed costs into their price, they
become profitable but prices rise for all consumers.
Figure S-1: Consumer prices under regulation and with different market scenarios.
0
1
2
3
4
5
6
7
8
9
10
Residential Commercial Industrial
¢/kWh
0
1
2
3
4
5
6
7
8
9
10
Regulated Generation Price
Market w/ all at marginal cost
Market w/ fix cost adder
Market w/ adder but Coal and Hydro Regulated
T&D
Plants are very reliant on the existence of higher-priced plants in order to make their profits in
a spot market. Even if a large segment of the capacity raises its price, it risks being undercut by
other plants unless they bid to just below the others’ marginal cost. The incentive for individual
plants to “cheat” and lower their bids can undermine the market power potential. Only if a
substantial majority of the participants in the market, especially those with higher costs, raise
their bids proportionately, do profits rise for all.
Withholding capacity can be successful in increasing profits, but only if the market is
constrained so that other producers (internal or external to the state) cannot offset the capacity
except at higher prices. Long-term contracts can mitigate the volatility of spot markets, with
prices likely approaching the regulated rates. In the actual market, companies may choose to sell
some of their generation under long-term contracts, some on the day-ahead or spot market, some
as either a spinning or non-spinning reserve, as well as save some for internal use if it is a
cogeneration project. All of these factors influence the final market and prices to consumers.
Oklahoma Restructuring Impact viii
REGULATION OF EXISTING LOW-COST PLANTS
Among the major beneficiaries of a change to pricing using market-based prices are the
existing low-cost producers, notably coal and hydro facilities. These two plant types would
receive prices much higher than their average costs plus a reasonable return. It might be feasible
during restructuring to mandate that they sell their power at cost plus a reasonable profit, instead
of at the full market rates. There are precedents of this in other states. For example, as part of its
restructuring, California required that the nuclear and hydro facilities owned by the investor-owned
utilities price their production at cost. While the rest of the production in the state became
very expensive this past year, the nuclear and hydro plants provided some measure of stability.
The utilities in many states undergoing restructuring have been faced with the problem of
paying for power plants that were more expensive than the market would bear. Oklahoma is
faced with the opposite situation; many of its existing power plants, especially the coal and hydro
plants, have costs much lower than the expected market prices. It may be advisable that some or
all of this difference be returned to customers in some fashion, through mechanisms such as
continued cost-based pricing (as we modeled), rebates following the sale of plants, or other
mechanisms. Figure S-1 shows the price impact if these plants continue to price based on their
regulated costs.
RESPONSE OF CUSTOMERS TO REAL-TIME PRICES
Customer response to high peak prices lowered the peak demand by roughly 9% in our model,
lessening the need for new capacity. The response of customers to real-time prices has a modest
effect on average prices paid. Its larger impact is on prices paid at the peak. In the case without
elasticity impacts, market prices were 120 ¢/kWh during the short time when all plants were at
full capacity. In the case with elasticity this price peaked at 100 ¢/kWh. With elasticity and
consequent flatter demand profile, peak prices do not have to rise as much to lower demand to
available capacity.
OVERALL ECONOMIC IMPACT TO STATE
The scenario with the highest price increases raised prices an average of 12 percent, in a
commodity that accounts for 2.3 percent of state production. Against this aggregate backdrop, it
is not surprising that the electricity rate changes have very small impacts on the overall economy
of the state. Depending on the price-change scenario, employment in the state could fall by three
or four one-hundredths of one percent while other property income could rise by about one-third
of one percent. The differences in impact across the scenarios also are small.
These impact projections are likely to be on the high side of actual, long-run impacts, since
the assumptions of the input-output framework, as well as assumptions we adopted for this study,
minimize the opportunities to substitute away from electricity in both final and intermediate
demands. We did not attempt to simulate the potential for substitution away from electricity into
natural gas for some energy uses, but over a five- to ten-year period, if some classes of rates
stayed twenty to twenty-five percent higher, some substitutions surely would occur in specific
uses such as heating, air conditioning and water heating.
Oklahoma Restructuring Impact ix
LIST OF FIGURES AND TABLES
Figure S-1: Consumer prices under regulation and with different market scenarios ...................vii
Figure 1: Oklahoma transmission lines with 345 kV lines highlighted (SPP 2001) ......................6
Figure 2: Oklahoma electricity supply curves for 1999 and 2010 ................................................9
Figure 3: Customer prices under each market scenario..............................................................13
Figure 4: Real-time market prices with all plants bidding their marginal costs ..........................14
Figure 5: Real-time market prices with plants adding 25% of fixed costs to bids.......................16
Figure 6: Change in Residential Load Duration Curve from real-time pricing and elasticity......22
Table S-1: Projected electricity supply and demand for Oklahoma in 2010. ...............................vi
Table 1: July 2001 listing of proposed plants for Oklahoma (DEQ 2001)....................................4
Table 2: Oklahoma electricity demand growth from 1999 to 2010 ..............................................5
Table 3: Estimated interstate transmission capacity from Oklahoma in 2010...............................6
Table 4: New Generating plants from Oklahoma DEQ and RDI..................................................7
Table 5: New plant cost and operating parameters.......................................................................8
Table 6: Average fuel prices for 2010, in 1999 $/MBtu...............................................................8
Table 7: Consumer regulated and market-based prices with plants bid marginal costs only .......12
Table 8: Plant operations and financial results with prices based on marginal costs...................13
Table 9: Operating and financial results with all plants at market rates and bids include 25% of
fixed costs .........................................................................................................................15
Table 10: Prices with all plants at market rates and bids include 25% of fixed costs..................16
Table 11: Prices with market prices including 25% fixed cost but existing coal and hydro plants
priced at costs ...................................................................................................................17
Table 12: Percent increase in prices market-based versus regulated, with most or all plants at
market rates.......................................................................................................................27
Table 13: Industrial sectors with output decreases, percents ...................................................... 28
Table 14: Industrial sectors with output increases, percents....................................................... 29
Table 15: Economic impacts of changes in electricity price schedules, percent change..............30
Oklahoma Restructuring Impact xi
ACRONYMS
CC Combined Cycle
CT Combustion Turbine
DEQ Oklahoma Department of Environmental Quality
EIA Energy Information Administration
EPRI Electric Power Research Institute
FERC Federal Energy Regulatory Commission
NERC North American Electric Reliability Council
O&M Operations and Maintenance
OCC Oklahoma Corporation Commission
ORCED Oak Ridge Competitive Electricity Dispatch model
ORNL Oak Ridge National Laboratory
RDI Resource Data International
ROE Return on Equity
SPP Southwest Power Pool
T&D Transmission and Distribution
Oklahoma Restructuring Impact 1
1 Introduction
In April 1997, the Oklahoma legislature passed a bill to restructure the state’s electric
industry, requiring that the generation sector be deregulated and allowing retail competition by
July 2002. Details of the market structure were to be established later. Senate Bill #220,
introduced in the 2000 legislature, provided additional details on this market, but the bill did not
pass. Subsequent discussions have identified the need for an objective analysis of the impact of
restructuring on electricity prices and the state’s economy, especially considering the experiences
of other states following restructuring of their electric systems.
Because of the recent experiences of other states undergoing restructuring (e.g., higher prices,
greater volatility, lower reliability), concerns have been raised in states currently considering
restructuring as to whether their systems are equally vulnerable. Factors such as local generation
costs, transmission constraints, market concentration, and market design can all play a role in the
success or failure of the market. Energy and ancillary services markets both play a role in having
a well-functioning system. Customer responsiveness to market signals can enhance the flexibility
of the market.
The purpose of this project is to provide a model and process to evaluate the potential price
and economic impacts of restructuring the Oklahoma electric industry. The goal is to provide
sufficient objective analysis to the Oklahoma legislature that they may make a more informed
decision on the timing and details of any future restructuring. It will also serve to inform other
stakeholders on the economic issues surrounding restructuring. The project is being conducted in
two phases. The Phase I report (Hadley 2001) concentrated on providing an analysis of the
Oklahoma system in the near-term, using only present generation and transmission resources.
This Phase II report looks further in the future, incorporating the potential of new generation
resources. Changes in the market structure due to additional capacity, pricing mechanisms, and
export markets are considered.
During the initial phase of the analysis, Oak Ridge National Laboratory (ORNL) developed a
benchmark or base case based on the existing set of plants, customer demands, and regulated
power prices. Generation and electric market data were gathered from the Department of
Energy’s Energy Information Administration (EIA), Resource Data International (RDI), the
North American Electric Reliability Council (NERC), and the Oklahoma Corporation
Commission (OCC). An ORNL-specialized model, the Oak Ridge Competitive Electricity
Dispatch (ORCED) model, was used to evaluate the marginal-cost-based prices for the state.
In this second phase of the study, we advanced the supplies and demands amounts to model
the year 2010. We considered the potential expansion of the electricity export market as
constrained by the available transmission capacity. Resulting power prices were adjusted to show
the impact of market power in bidding and the continued regulation of some power plants. Using
the real-time prices, we adjusted customer load profiles based on their price elasticity and
reevaluated the impact of restructuring on consumer prices. Lastly, we used an input/output
economic simulation of the Oklahoma economy to determine the broader economic impacts of
changes in prices.
Oklahoma Restructuring Impact 3
2 Background
2.1 Recap of Phase I Analysis
The Phase I study provided a view of the Oklahoma electricity market if restructuring
occurred in 1999. Customer demands and power plant production were found from existing
reports submitted to the Federal Energy Regulatory Commission (FERC) and Energy
Information Administration (EIA). Existing plants were allowed to price based on the marginal
cost of the highest-cost plant operating at any one time.
The analysis identified two key issues. First, much of the existing capacity is low-cost coal.
Under existing regulated pricing these plants receive revenues sufficient to pay costs plus a
reasonable return on investment. In a restructured market with prices set by the marginal
producer, revenues for the low-cost coal plants increased greatly. This was reflected in a general
rise in electricity prices of around 1¢/kWh in the base scenario. Furthermore, market-based
electricity prices are more sensitive to the price of natural gas. With an increase in gas prices of
53%, market prices rose 1.5¢/kWh while regulated prices (that average all production costs) rose
0.5¢/kWh. As a consequence, market prices became 2¢/kWh higher than regulated prices.
Sensitivities were also run on the availability of coal-fired capacity, raising it from the
historical value of the existing plants to broader industry standards. The increase in low-cost
production lowered both the regulated and market prices. An interesting detail was that the
increased production from the coal plants actually lowered their profitability because of their
effect on market prices. Other plants also suffered lower profits, threatening their continued
operation. This touches on the issue of market power, which will be looked at in more detail in
this paper.
2.2 Rise of Merchant Power
Generation capacity is growing throughout the country. According to the RDI NewGen
database (RDI 2001b), over 390 GW of capacity are planned or under construction in the U.S.
Much of this capacity is being built not by regulated utilities, but by independent power
producers. These producers sell their generation either through long-term contracts to utilities or
in shorter-term or spot markets. Within Oklahoma, 98% of the proposed new construction is by
merchant power producers.
As part of restructuring, power plants may sell directly to end-use consumers. As with
utilities, consumers may sign bilateral long-term contracts or purchase through a spot market.
Small consumers may choose to aggregate their demands to better take advantage of the market.
These aggregators may be existing utilities, municipalities, or even new organizations that
provide this service.
Even without restructuring in all states, merchant power is a rising force within the electric
power industry. Traditional utilities have been reluctant to construct new facilities, due to
uncertainty of the market and potentially inadequate returns on their investment. Some utilities
have created unregulated subsidiaries to control their generation assets and to build additional
plants. They choose to use their expertise in owning and operating power plants by competing in
Oklahoma Restructuring Impact 4
the open market outside of their regulated territories. Other companies have also entered the
market, building either stand-alone merchant plants or cogeneration plants within an existing
industrial facility.
In some states that are undergoing restructuring, the utilities have been forced to sell some or
all of their generation. This was done to avoid the utilities obtaining too much market power
through combined ownership of transmission assets and a large share of the generation assets.
Auctions have been held to sell the plants to multiple companies. The prices paid helped to
determine the asset values and stranded costs of the utility. These plants have frequently been
purchased by the unregulated subsidiaries of utilities that are located elsewhere in the country or
overseas, or by independent power producers.
In Oklahoma, there have been a large number of plants proposed for construction in the
coming years. The Oklahoma Department of Environmental Quality (DEQ 2001) releases a
monthly report of the proposed plants that shows the air permit status, capacity, and type of plant
(Table 1). Most of these plants are to be built by companies that are not the regulated utilities
within the state. As such they will be able to sell their power either through contracts with
existing utilities, on the wholesale spot market, or if restructuring occurs, directly to consumers.
Table 1: July 2001 listing of proposed plants for Oklahoma (DEQ 2001)
Facility Permit
Status
Fuel Gen. Cap.
MW
Base Units(Comb.Cycle)
AECI – Chouteau Issued GAS 530
Cogentrix- Jenks Issued GAS 800
C&SW – Oologah Issued GAS 492
Calpine – Coweta Issued GAS 1,000
Duke – Newcastle Issued GAS 520
Energetix-Arcadia Proposed GAS 1,100
Energetix Thunderbird Issued GAS 865
Kiowa – Kiamichi Issued GAS 1,200
Smithcogen – Pocola Proposed GAS 1,200
Smithcogen. - Lawton Tech. Rev. GAS 600
Energetix – Webbers Falls Tech. Rev. GAS 825
Tenaska - Seminole Tech. Rev. GAS 1,200
Energetix – Great Plains Tech. Rev. GAS 900
Duke - Stephens Admin Rev GAS 620
Total Combined Cycle 11,852
Peaking Units(Simp. Cycle)
OG&E - Horseshoe Issued GAS 90
OneOK - Edmond Issued GAS 320
KM Pwr - Pittsburg Issued GAS 550
WFEC – Anadarko Issued GAS 94
Mustang – Mustang Draft GAS 310
Mustang - Harrah Admin Rev GAS 310
Total Simple Cycle 1,674
Grand Totals 13,526
Oklahoma Restructuring Impact 5
3 Oklahoma Market Data for 2010
3.1 Demand Growth within Oklahoma
According to the Electric Power Annual 1999 (EIA 1999), total retail demand for Oklahoma
in 1999 was 46,700 GWh. According to the EIA’s Annual Energy Outlook 2001 (EIA 2000),
overall electric power demand in the Southwest Power Pool is expected to grow overall by 26%
between 1999 and 2010, representing an annual growth rate of 2.1% (Table 2). Each sector
(residential, commercial, and industrial) has different levels of growth, depending on a variety of
factors such as economic development and changes in technology.
Table 2: Oklahoma electricity demand growth from 1999 to 2010
1999 Sales
GWh
Annual
escalation
2010 Sales
GWh
Losses Busbar
GWh
Peak
MW
Residential 18,300 2.2% 23,400 8% 25,400 7,510
Commercial 12,400 2.6% 16,500 6% 17,500 3,350
Industrial 13,300 1.4% 15,500 5% 16,300 2,910
Other 2,800 2.1% 3,500 6% 3,700 570
Total 46,700 2.1% 58,900 63,000 14,340
3.2 Export of Power and Transmission Capacity
Based on the analysis in Phase I, total exports of power from Oklahoma in 1999 were 4,800
GWh, with a peak demand of 800 MW. A simple expansion of this demand using the growth rate
from above through 2010 would give sales of 6,500 GWH and peak capacity of 990 MW.
Consequent total demand would be 15,300 MW. However, proposed expansions of capacity
greatly exceed this amount, as shown in Table 1. Since power plant capacity is projected to be
much higher, the question arises as to the how much could exports increase, given transmission
constraints.
There are currently seven 345 kV lines that cross Oklahoma state lines according to the
Southwest Power Pool (SPP) (Figure 1).1 An approximate line rating for conventional three-phase
lines at 345 kV is 600 MW (EPRI 1982), although this value can vary greatly depending
on the length and materials used for the line. These lines could therefore accommodate 4,200
MW of interstate transmission. An additional 345 kV line is planned for 2006 (Northwest to
Harrington), which could provide an additional 600 MW of transmission capacity.
There is also one 230 kV line that crosses into the panhandle of Texas (Elk City to
Harrington-Nichols). The estimated capacity for this line is 200 MW. In addition, several 138 kV
lines cross the state border that could be used for interstate energy transfer. Using an
approximate line rating for 138 kV lines of 75 MW, the estimated maximum capacity at 138 kV
is 1,050 MW.
1 The seven current 345 kV lines are Woodring to Wichita, Northeastern to Neosho, GRDA 1 to Flint
Creek, Clark to Chambers Springs, Muskogee to Ft. Smith, Valliant to Lydia, and Lawton to Oklaunion.
Oklahoma Restructuring Impact 6
As shown in Table 3 below, it appears that by 2010 Oklahoma would have a maximum
interstate transmission capacity of approximately 6,000 MW. Hirst and Kirby give higher
estimates of the capacity for various voltage lines, closer to 900 MW per 345 kV line (Hirst and
Kirby 2001). However, their analysis was based on thermal limits for short lines, less than 100
miles. As lengths increase, the maximum transmission capability drops because of voltage or
stability limits. Furthermore, actual combined capabilities can be different from simply the sum
of the individual lines, and sales potentials will depend on the markets that the lines enter. A
more detailed analysis of the actual transmission limits in and out of Oklahoma may be
necessary to establish the potential for exports.
Figure 1: Oklahoma transmission lines with 345 kV lines highlighted (SPP 2001)
Table 3: Estimated interstate transmission capacity from Oklahoma in 2010
Line Voltage (kV) Number of
lines
Approximate line
rating (MW)
Estimated maximum
capacity (MW)
345 8 600 4,800
230 1 200 200
138 14 75 1,050
Total 23 - 6,050
3.3 Generation Supply
Generation capacity is growing in Oklahoma. Plans for new plants indicate a broad expansion,
roughly doubling the amount of capacity available. The number of plants listed in Phase I for
1999 totaled 13,430 MW. In 2010, most of these plants are expected to still be operating,
Oklahoma Restructuring Impact 7
although 1,260 MW of plants were removed from the list due to either retirement or
refurbishment. This left 12,170 MW of capacity from existing plants in 2010.
There were three sources of data on new plants for Oklahoma. The most complete source used
was the list of new plants from the Oklahoma DEQ (Table 1). These were compared to lists of
plants from Resource Data International’s NewGen (RDI 2001a) and PowerDat databases (RDI
2001b) that give capacity, fuel, and expected on-line date. In some cases either the plant
capacities did not match with the DEQ data, the plants did not have the same name, or on-line
dates were not provided. The DEQ capacities were used, with on-line dates of 2004 for those
without known dates. The total new capacity added was 11,852 MW of combined cycle (CC)
and 1,674 MW of combustion turbines (CT), in line with the DEQ estimates (Table 4).
Table 4: New Generating plants from Oklahoma DEQ and RDI
Facility Permit
Status
Fuel Gen. Cap.
MW
RDI Gen.
Cap. MW
RDI On-line
Date
Base Units(Comb.Cycle)
AECI – Chouteau Issued GAS 530 530 2004
Cogentrix- Jenks Issued GAS 800 800 2002
C&SW – Oologah Issued GAS 492 300 2001
Calpine – Coweta Issued GAS 1,000 1,000 2002
Duke – Newcastle Issued GAS 520 500 2001
Energetix-Arcadia Proposed GAS 1,100 1060 2003
Energetix Thunderbird Issued GAS 865 825 2003
Kiowa – Kiamichi Issued GAS 1,200 1200 2003
Smithcogen – Pocola Proposed GAS 1,200 600 2003
Smithcogen. - Lawton Tech. Rev. GAS 600 600 2003
Energetix – Webbers Falls Tech. Rev. GAS 825 825 –
Tenaska - Seminole Tech. Rev. GAS 1,200 1,200 –
Energetix – Great Plains Tech. Rev. GAS 900 500 2004
Duke - Stephens Admin Rev GAS 620 ?
Total Combined Cycle 11,852
Peaking Units(Simp. Cycle)
OG&E - Horseshoe Issued GAS 90 96 2000
OneOK - Edmond Issued GAS 320 300 2001
KM Pwr - Pittsburg Issued GAS 550 550 2003
WFEC – Anadarko Issued GAS 94 90 –
Mustang – Mustang Draft GAS 310 ?
Mustang - Harrah Admin Rev GAS 310 ?
Total Simple Cycle 1,674
Grand Totals 13,526
Because the actual efficiencies and costs for these plants are not known, we used
representative values from a recent study on future energy use, Clean Energy Futures, written by
five national laboratories including ORNL (Inter-Laboratory Working Group 2000) and from the
Generating Availability Data System from NERC (NERC 1998). Heat rates, fixed and variable
Oklahoma Restructuring Impact 8
operations and maintenance (O&M) costs, capital costs were , and outage rates for combined
cycle and combustion turbines were assigned to the plants in Table 4 based on the year they
entered into service (Table 5).
Table 5: New plant cost and operating parameters
Effici-ency
Capital
Nom $/kW
Variable O&M
1999 ¢/kWh
Fixed O&M
1999 $/kW
Forced
Outage Rate
Planned
Outage Rate
Combined Cycle
2001 49.7% 476 0.05 15.6 3.4% 10.3%
2002 50.1% 491 0.05 15.6 3.4% 10.3%
2003 50.5% 505 0.05 15.6 3.4% 10.3%
2004 51.0% 521 0.05 15.6 3.4% 10.3%
Combustion turbine
2001 37.8% 354 0.01 6.4 3.0% 10.8%
2002 38.3% 365 0.01 6.4 3.0% 10.8%
2003 38.8% 376 0.01 6.4 3.0% 10.8%
2004 39.3% 387 0.01 6.4 3.0% 10.8%
Average fuel prices were raised from the 1999 values used in Phase I to 2010 values by
applying the expected increase from the AEO2001 for the Oklahoma region (Table 6). (Note that
coal prices are expected to increase at less than the general inflation rate so have a negative
escalation rate.) In Phase I, the existing plants used fuel prices based on their reported amounts
for 1999. We escalated each plant’s cost by the rate shown in Table 6. The new plants used the
average prices as shown in the table.
Table 6: Average fuel prices for 2010, in 1999 $/MBtu
1999 Avg. Fuel Cost,
$/MBtu
Annual Escalation Above
General Inflation
2010 Avg. Fuel Cost,
1999$/MBtu
Gas 2.73 1.8% 3.34
Coal 0.94 -1.0% 0.84
Dist. Oil 2.06 1.0% 2.29
Although the plants are listed as in planning or under construction, it is not known whether all
will be built. For example, three plants are proposed for construction in the Lawton, OK, area.
However, according to minutes of the Lawton city council there may be water limits requiring
auctioning of available water (Lawton City Council 2001). In scenarios where there was excess
capacity, some plants were removed from the list, beginning with those with the latest on-line
date. However, in reality, other plants may have their dates or capacities modified, or
transmission capacity may be added through new lines or modifications. Also, the costs and
operating parameters are likely to be different than the estimates used here. For these reasons, it
is important to realize that the results from this study should not be applied to specific plants.
The plants within Oklahoma were separated into 141 units, each with its own capacity and
operating parameters. When placed in order of increasing marginal cost, they create a supply
Oklahoma Restructuring Impact 9
curve. Figure 2 shows the supply curve for the plants in 2010, as well as the 1999 curve from the
Phase I report. The large increase in capacity at essentially the same marginal price has
interesting consequences for the profitability of the plants, as described later.
Figure 2: Oklahoma electricity supply curves for 1999 and 2010
0
2
4
6
8
10
12
14
0 5,000 10,000 15,000 20,000 25,000 30,000
Power Level, MW
Price, ¢/kWh
2010 Supply Curve
1999 Supply Curve
New Combined Cycle
New Combustion
Turbine
Existing Coal
Existing Gas
CT and ST
Oklahoma Restructuring Impact 11
4 ORCED Analysis
The Oak Ridge Competitive Electricity Dispatch (ORCED) model was developed at Oak
Ridge National Laboratory to examine numerous facets of a restructured electricity market
(Hadley and Hirst 1998). The model is a complex Excel spreadsheet that takes the inputs on
supply and demand described above and dispatches plants to meet the defined demands for a
single year of operation. Further details on the calculations involved are included in the Phase I
report (Hadley et al., 2001).
Several versions of the model have been developed over the years depending on the needs of
the study. For this study we used a version that models a single region without internal
transmission constraints. It can handle up to 200 power plants and models two seasons, a peak
and an off-peak. For the Phase II analysis the model was modified to allow individual plants to
sell some or all of their power at either the market-based rate or on a contractual, fixed cost basis.
Also, the calculation of the bid prices that plants use in the spot market was modified to enable
varying amounts of fixed costs to be incorporated into the bid.
4.1 Determining Production scenario
In order to create a credible scenario for 2010 we must first establish the expected demand and
production levels. For the initial run, we used the internal demands as shown in Table 2 and kept
exports equal to 11% of internal retail demands as in 1999. This resulted in a peak demand of
15,300 MW. If all planned capacity is built, the total capacity available is 25,600 MW, resulting
in a reserve margin of 67%. At such a high level, many of the plants will never be called upon to
produce, which makes this scenario unlikely. Either demands must increase, supply decrease, or
both.
To increase demands, we raised the amount of exports from a peak demand of 990 MW to
6,030 MW, as described in Section 3.2 above. We left the load factor the same as in 1999 so that
total sales also scaled up by a factor of five as well. This makes total exports equal to 67% of
retail sales, versus 11% in 1999, and raised peak demand to 20,380 MW. Even with this
expansion to full capacity of the transmission lines, the reserve margin was 25.8%. Many plants
were still never called upon or for only a few hours in the year.
We lastly lowered the supply capacity by removing 3,545 MW of planned combined cycle
construction (the last four CC plants in Table 4). This lowered the reserve margin to 8.4%, which
is slightly lower than the 10.0% in the 1999 case. While too high of a value can cause some
plants to run so rarely that they are not economic, too low of a value can cause reliability
concerns. For example, California declares Stage One Emergencies when the reserve margin
drops below 7%. Further information on this topic can be found in the paper on generation
adequacy (Hirst and Hadley 1999).
4.2 Determining price scenarios
Once supplies and demands were set, we considered the prices to customers and profitability
of the plants. There are two issues concerning prices, what are the overall prices paid by
customers in a restructured market compared to a regulated market, and do new plants receive
Oklahoma Restructuring Impact 12
sufficient revenues to justify their construction and operation. If prices are low to consumers but
new plants are losing money, then this is not a viable scenario. We set the expected return on
equity for the new combined cycle plants at 14% after taxes. Returns significantly below that
would discourage investors from funding their construction.
4.2.1 All plants at marginal-cost based market price
First, we ran a scenario that had all plants bid their marginal prices on the spot market. This is
similar to the pricing used in Phase I. This scenario provides lower prices to residential
consumers than the regulated prices, although other customers pay slightly higher prices. Table 7
first shows the transmission and distribution (T&D) charge to customers, based on results from
the Phase I study. These were not changed from the 1999 values because the focus of this study
is not on changes in T&D prices. The next column shows the generation prices if system-wide
revenue requirements are allocated between customer categories based on their energy purchases
and peak demand requirements. The third column is the sum of the previous two. The
restructured generation price is the average price paid by each customer category if charged
based on the time-varying market price, with adjustments for plants that have prices fixed by
long-term contracts. The total restructured price is the sum of the generation and T&D prices,
and the difference between restructured and regulated prices are shown in the last column. The
regulated and market prices are shown in Figure 3.
Table 7: Consumer regulated and market-based prices with plants bid marginal costs only
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.83 7.17 3.26 6.59 -0.57
Commercial 2.89 3.14 6.02 3.16 6.05 0.03
Industrial 1.00 3.03 4.03 3.13 4.13 0.10
In this scenario with all plants charging marginal costs, the new plants lost money because
they could not recover their fixed costs. The combined cycle plants had a return on equity (ROE)
of -2%, well below the expected value of 14%. In Table 8 the first column shows the capacity for
each major plant type. Hydro includes both hydro and pumped storage facilities. The “Old”
designation is for plants built prior to 2000 and “New” for plants built in 2000 or later. The
capacity factor indicates the amount of generation the plant produced as compared to if the plant
ran at full power 100% of the year. The percentage of time on the margin indicates for what
percentage of the year each plant type was the most expensive plant operating and so setting the
market-clearing price. The average price is the revenues received by each plant type divided by
its production, while the average cost is the sum of its fuel, O&M, depreciation, interest charges,
taxes, and a “reasonable” return on equity (shown in the last column). The ROE is the net
income received by the plants (escalated to 2010 $) divided by the equity invested in the plants.
Oklahoma Restructuring Impact 13
Figure 3: Customer prices under each market scenario
0
1
2
3
4
5
6
7
8
9
10
Residential Commercial Industrial
¢/kWh
0
1
2
3
4
5
6
7
8
9
10
Regulated Generation Price
Market w/ all at marginal cost
Market w/ fix cost adder
Market w/ adder but Coal and Hydro Regulated
T&D
Table 8: Plant operations and financial results with prices based on marginal costs
Plant Type
Capa-city
MW
Capa-city
Factor
%
Time
on
Margin
%
Price
Receiveda
¢/kWh
Marginal
Costb
¢/kWh
Total
Costc
¢/kWh
ROE
%
Regu-lated
ROE
%
Coal 5156 76 0 2.77 0.97 1.45 437.3 11.9
Hydro 1035 29 0 3.45 0.37 1.52 772700 0.0
Oil 39 19 0 3.53 2.69 4.39 -598.6 11.0
Gas ST 4625 5 13 5.49 3.90 6.28 -33.9 11.0
Gas CC/Old 909 29 10 3.79 3.03 3.67 41.5 12.9
Gas CT/Old 343 6 1 4.57 3.38 7.23 -93.0 11.0
Gas CC/New 8309 79 54 2.89 2.31 3.62 -2.0 14.0
Gas CT/New 1674 22 21 3.92 2.90 6.14 -4.2 13.9
a Price received is total revenue divided by sales.
b Marginal cost is variable and start-up costs of operation
c Total cost includes operations, depreciation, interest, taxes, and expected return on equity
Because there was so much new combined cycle capacity, these plants were on the margin for
54% of the year, even though they operated close to capacity with a capacity factor of 79%.
Being similar plants of similar age, their marginal cost and consequent bid prices were also
similar, at about 2.3 ¢/kWh. This can be seen in Figure 2, with the long flat part of the supply
curve showing the capacity from the new combined cycle plants.
Oklahoma Restructuring Impact 14
Though their marginal costs were only 2.3 ¢/kWh, their total cost including all operations and
capital-related costs were about 3.6 ¢/kWh. If they only received their marginal costs during the
portion of the year they were on the margin, there was very little time for them to recover all of
the fixed costs and prices would have to be much higher during that part of the year.
Figure 4 shows the market-based prices over the year if all plants solely bid their marginal
costs. As can be seen, the prices are below 3.6 ¢/kWh for most of the year. Only during about
30% of the summer peak season, or just 9% of the year, do prices rise above the total cost of new
combined cycle plants, as more expensive combustion turbines and gas-fired steam plants set the
price.
Figure 4: Real-time market prices with all plants bidding their marginal costs
0
2
4
6
8
10
12
14
0 20 40 60 80 100
Percent of Season
¢/kWH
Peak Season, Jun 1-Sep 16, 30% of year
Off-Season, Jan 1-May 31, Sep 17-Dec 31, 70% of year
4.2.2 Marginal plus added fixed costs in bid price
A plant may increase the prices it receives in several ways. First, the owners could bid a
higher price into the spot (and day-ahead) markets. If accepted, these prices will give higher
revenues. The danger is that other plants will then undercut the price bid. Consequently, the plant
may not be called on to run as often and lose revenue. If a plant or set of plants has sufficient
market power, then they may be able to raise their prices without being significantly undercut by
other generation sources.
With new plants, either gas CC or CT, setting the marginal prices 75% of the time, it is clear
that they would need to incorporate their fixed costs into their bids in some way. To simulate an
added bid factor, we added 25% of their fixed costs to the variable cost of each plant in their bid
price. In order to convert fixed costs to variable we used the capacity factor for each plant from
the scenario when they only bid their variable cost. As a consequence, newer plants, with higher
Oklahoma Restructuring Impact 15
fixed capital costs, had higher increases in their bids than the older plants. This slightly changed
the loading order and consequent capacity factors that each plant actually had.
The value of 25% of fixed cost was determined because at that amount, the new CC plants
earned close to 14% on their equity (Table 9). New gas CT’s still make somewhat less than their
expected return, but this analysis does not include extra revenue from ancillary services such as
spinning reserve. These extra services, which are most often provided by CT’s, could raise the
total ROE. Older plants, especially coal and hydro, make large returns on their equity. (Hydro as
modeled has essentially no equity.)
Table 9: Operating and financial results with all plants at market rates and bids include
25% of fixed costs
Plant Type
Capa-city
MW
Capa-city
Factor
%
Time
on
Margin
%
Price
Receiveda
¢/kWh
Marginal
Costb
¢/kWh
Total
Costc
¢/kWh
ROE
%
Regu-lated
ROE
%
Coal 5156 76 0 3.41 0.97 1.45 652.8 11.9
Hydro 1035 29 0 4.69 0.39 1.54 1260730 0.0
Oil 39 17 0 5.30 2.71 4.62 398.9 11.0
Gas ST 4625 7 20 8.08 3.71 5.40 213.1 11.0
Gas CC/Old 909 37 12 4.40 2.98 3.48 255.5 12.9
Gas CT/Old 343 6 1 8.47 3.45 7.64 51.7 11.0
Gas CC/New 8309 79 54 3.62 2.31 3.63 13.9 14.0
Gas CT/New 1674 15 12 6.45 2.91 7.79 6.7 13.9
a Price received is total revenue divided by sales.
b Marginal cost is variable and start-up costs of operation plus 25% of fixed cost
c Total cost includes operations, depreciation, interest, taxes, and expected return on equity
The older gas plants also make very good returns, partly from the rise in price from the new
CT’s, and partly from the increase in bid prices from plants operating for only a small part of the
year. For example, adding 25% of the fixed cost of $15/kW-year to a plant that operates 1% of
the year, or 88 hours, increases its bid price by 4.3 ¢/kWh . If the plant runs 10% of the year the
added part is only 0.43 ¢/kWh; if it runs 0.1%, or 9 hours, the added amount is 43 ¢/kWh. This
impacts not only its own profitability but the prices of all plants running at that time.
Figure 5 shows the real-time prices over the year under this scenario. Prices are slightly higher
in the off-season and during the low-demand period of the peak season, as compared to Figure 4.
However, prices rise higher and more rapidly in the peak season as the plants with low capacity
factors are called on and their bids include a higher proportion of fixed costs.
However, in this scenario, prices to all customers increased over what they would pay under
regulated rates (Table 10 and Figure 3). So while new plants would be solvent in this scenario,
the price impact on consumers makes this scenario less feasible.
Variations on the percentage of fixed costs added can be run, including having some plants,
such as the older plants, not including the adder. However, as fewer plants include the added cost
Oklahoma Restructuring Impact 16
then they by necessity must include a higher percentage in order to recoup their fixed costs. This
causes them to be called on less often since the plants without the expense in their bid now are
priced lower. The end effect is that new, efficient plants are called on much less frequently than
more expensive, older plants, which is likely not what the reality would be.
Figure 5: Real-time market prices with plants adding 25% of fixed costs to bids
0
2
4
6
8
10
12
14
0 20 40 60 80 100
Percent of Season
¢/kWH
Peak Season, Jun 1-Sep 16, 30% of year
Off-Season, Jan 1-May 31, Sep 17-Dec 31, 70% of
Table 10: Prices with all plants at market rates and bids include 25% of fixed costs
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.84 7.18 4.18 7.52 0.34
Commercial 2.89 3.15 6.04 4.03 6.92 0.88
Industrial 1.00 3.04 4.04 3.98 4.99 0.95
4.2.3 Regulated pricing of existing coal and hydro plants
Among the major beneficiaries of a change to pricing using market-based prices are the
existing low-cost producers, notably coal and hydro facilities. As shown in Table 9, these two
plant types receive prices much higher than average costs, while having no influence on the
market price since they are never on the margin. It might be feasible during restructuring to
mandate that they sell their power at cost plus reasonable profit, instead of at the full market
rates. There are precedents of this in other states. For example, as part of its restructuring,
Oklahoma Restructuring Impact 17
California required that the nuclear and hydro facilities owned by the investor-owned utilities
price their production at cost. While the rest of the production in the state became very expensive
this past year, the nuclear and hydro plants provided some measure of stability.
To examine the impact of having coal and hydro plants sell power at cost instead of market,
we modified ORCED so any or all plants could price at a fixed price. We set the price for coal
and hydro plants so that they would recover their costs and reasonable return on equity. These
plants as modeled actually have very little equity in them by 2010, both because of their age and
because the plants owned by government entities were modeled as being debt-financed so with
essentially no equity per se. As a result, customer prices dropped such that residential consumers
saw prices 0.34 ¢/kWh lower under restructuring than under regulation, and other customer saw
only modest increases (Table 11 and Figure 3). Coal and hydro plants had their average price
drop to their costs and ROE’s of 11.9% and 0% respectively; while all other plants had the same
returns as in Table 9.
Table 11: Prices with market prices including 25% fixed cost but existing coal and hydro
plants priced at costs
T&D Regulated
Generation
Price
Total
Regulated
Price
Restructured
Generation
Price
Total
Restructured
Price
Differ-ence
Residential 3.34 3.84 7.18 3.51 6.85 -0.34
Commercial 2.89 3.15 6.04 3.25 6.14 0.10
Industrial 1.00 3.04 4.04 3.19 4.19 0.15
4.2.4 Contracts versus spot-market pricing
An alternative to plants selling on the spot market is for plants to sign long-term contracts for
some or all of their production. The prices may include a fixed cost for the capacity of the plant
and a variable cost for the actual production. This is similar to the system-wide pricing that
occurs under regulated rates, but on a plant level. Total revenue requirements are calculated by
summing the fixed and variable costs of operations, including capital costs such as depreciation,
interest, and a reasonable rate of return. The revenue requirements are then charged to customers
either through a single energy-related price or through separate demand and energy charges.
(This is a simplification of the actual rate-setting process and types of rates created.)
As an example, below is a statement from the 10-K form from Cogentrix on their power
project financing and contracts:
PROJECT AGREEMENTS, FINANCING AND OPERATING
ARRANGEMENTS FOR OUR OPERATING FACILITIES
Project Agreements
Our facilities have long-term power sales agreements to sell electricity to electric
utilities and power marketers. A facility's revenue from a power sales agreement
Oklahoma Restructuring Impact 18
usually consists of two components: variable payments, which vary in accordance
with the amount of energy the facility produces, and fixed payments that are
received in the same amounts whether or not the facility is producing energy.
Variable payments, which are generally intended to cover the costs of actually
generating electricity, such as fuel costs, if supplied by the operating facility, and
variable operation and maintenance expense, are based on a facility's net electrical
output measured in kilowatt hours. Variable payment rates are either scheduled or
indexed to the fuel costs of the electricity purchaser and/or an inflationary index.
Fixed payments, that are intended to compensate us for the costs incurred by the
project subsidiary whether or not it is generating electricity, such as debt service
on the project financing, are more complex and are calculated based on a declared
production capability of a facility. Declared production capability is the electric
generating capability of a plant in megawatts that the project subsidiary
contractually agrees to make available to the electricity purchaser. It is generally
less than 100% of the facility's design production capability dictated by its
equipment and design specifications. Fixed payments are based either on a
facility's net electrical output and paid on a kilowatt-hour basis or on the facility's
declared production capability and can be adjusted if actual production capability
varies significantly from declared production capability. (Cogentrix 2000)
If the long-term contracts are based on the company receiving their expected rate of return,
there is little difference in prices between the regulated market price and contract price. We ran
ORCED with all plants selling under long-term contracts at prices based on their expected
returns. As a result, the restructured prices to customers were the same as the regulated prices,
and all plants made their regulated ROE. If that is the case, the main difference between a
restructured market and regulated market is that the individual plants may contract directly with
end-customers rather than just the local utility or wholesale marketers.
The question then arises on whether a plant would choose to sign long-term contracts or bet
on the spot market for pricing. And, if it were to sign long-term contracts, would they be priced
close to their costs with a reasonable profit, or would they try to set prices close to the expected
average spot price? Would they be willing to sacrifice some profit for the sake of firm prices?
Similarly, how much more are customers willing to pay over the expected spot prices in order to
get some price surety? These questions are asked daily by generators, outside investors,
marketers, and utilities in today’s market. Different business plans and portfolios are developed
in a complex combination of long-term and short-term purchases, generation, and hedging
strategies. Companies may choose to sell some of their generation under long-term contracts,
some on the day-ahead or spot market, some as either a spinning or non-spinning reserve, as well
as save some for internal use if it is a cogeneration project. All these factors influence the final
market.
4.3 Market power: modified bids and withheld capacity
In establishing the base case we modeled that all plants would include a portion of their fixed
costs in their price. This is a simple version of market power in that all suppliers tacitly agree to
Oklahoma Restructuring Impact 19
increase their bids. Two more complex mechanisms for plant owners to exert market power are
to raise their bid prices as a group or to withhold some of their lower cost capacity.
4.3.1 Group bids
In a more complex market scenario, the owners of new combined cycle plants may recognize
that their bids can be raised to just below the cost of the next more expensive technology. The
marginal costs of the new CC plants are around 2.35 ¢/kWh; the next most expensive major plant
type (new Gas CT) have marginal costs around 2.86 ¢/kWh. If the CC plants were to raise their
bids to 2.85 ¢/kWh, they should still have roughly the same sales, yet earn an additional 0.5
¢/kWh when they are the marginal producers.
Table 8 above showed the results if all plants just bid marginal prices. If just the new CC
plants raised their prices to 2.85 ¢/kWh, their ROE does improve from the –2% in the table to
3.4%. However, the higher-cost plant types see little change in their ROE’s since their prices
were no different than before. Coal and hydro facilities receive an extra windfall as the average
price goes up from 2.77 to 3.03 ¢/kWh. Thus, even if all new combined cycle operators, as
shown in Table 4, raised their prices together to just below the gas CT prices, there is only some
improvement in their profitability.
As a further step, we considered if all new CC and CT plants raised the bids together to the
level of the next technology. Gas Steam plants begin entering the market at bid prices of 3.26
¢/kWh. Raising the CC and CT bids to just below this amount allows them to collectively still
operate the same amount while making an additional 0.4 ¢/kWh. CC plants’ ROE rose to 9.4%
and CT plants rose from –4.2 to -3%.
Lastly, what happens if all the new plants bid 3.25 ¢/kWh but one? Suppose that one of the
new CT’s that normally would have operated in a peaker mode with a capacity factor of 25%
chose to bid its marginal cost instead. It then becomes a baseload unit running 86% of the year,
and earning +5% ROE instead of -2%. This is a strong incentive for individual plants to lower
their bids, with the hopes that no one else does.
Plants are very reliant on the existence of higher-priced plants in order to make their profits in
a spot market. Even if a large segment of the capacity raises its price, it risks being undercut by
other technologies unless they bid to just below the others’ marginal cost. The incentive for
individual plants to “cheat” and lower their bids can undermine the market power potential. Only
if a substantial majority of the participants in the market, especially those with higher costs, raise
their bids proportionately, do profits rise for all.
4.3.2 Withholding capacity
The other mechanism by which market power can be exercised is through the withholding of
capacity. If low cost producers choose to not bid a portion of their capacity, then the market-clearing
price will be higher as more expensive plants replace the lost capacity. The individual
plant that does not run will lose money, but the other plants that the producer owns may earn
enough more through higher prices to compensate.
Oklahoma Restructuring Impact 20
We ran two examples of capacity withholding: one of a company that owns multiple new
plants, and one of an existing producer. In the first case, we lowered the production 10% from
the 600 MW Lawton plant owned by Smith Cogeneration. We had modeled it at slightly lower
cost than the Pocola plant, so reducing the Lawton plant by 52 MWyr increased the production
of Lawton by 8 MWyr. Plants owned by other companies supplied the rest of the missing
production. Because the replacement power was more expensive, the average price to customers
increased 0.06 ¢/kWh. Smith’s revenues declined $6 million, but since they did not have the
expense of production, their net income actually rose $2 million. Their overall ROE increased
from 12.7% to 13.2%.
In the other example, we reduced the production from the 1015 MW Sooner coal plant owned
by Oklahoma Gas and Electric by 10%. This led to an increase in production from other plants,
owned by both OG&E and others. OG&E’s ROE increased from 46% to 66%, despite the 5%
lower overall production. Average prices to customers increased 0.12 ¢/kWh as higher-cost
plants provided a larger share of the total. The coal plant’s production declined by 76 MWyr, but
other plants owned by the utility, most notably their peaker gas steam plants, increased
production by 6 MWyr. Since these other plants are unregulated and very profitable in this
scenario, the utility’s overall profitability increased.
A key reason for improvement in the utility’s ROE was the unintended consequence of
regulating the price from one plant but not others. OG&E earned the regulated 11% return on
their coal plant regardless of its actual production. By reducing its output, other plants owned by
OG&E that were not regulated in the scenario increased their production and their revenues
(especially since prices increased as well), while the coal plant returns were not reduced.
Because we are only considering the production from Oklahoma plants as substitutes for the
lost production, in both cases, reducing production had the effect of raising net income. Prices
rose sufficiently to offset the lost income from the production. In the broader regional electricity
market, however, capacity from outside the state may enter the market to make up the lost sales
without causing a significant increase in price. This depends on the cost and supply of extra
generation in the outside market. In the larger regional market, the utilities have less impact on
the overall reserves. Since we modeled a significant amount of sales into the outside market,
withholding capacity may simply lower external sales with no effect on overall prices. Also, if
the plants continued to operate at lower capacity, new construction would enter into the market
to more permanently negate this market power. While we used two specific utilities in these
examples, we do not wish to imply that they and they alone wield market power in the Oklahoma
electricity market.
4.4 Elasticity and real-time pricing
Most experts on restructuring recommend that customers have real-time pricing available
(Taylor and VanDoren 2001, Hirst 2001). The actual cost of generation can vary greatly between
seasons or even hours. When customers are only aware of the average price from the previous
month, they have little knowledge or incentive to adjust their demands as the real-time price
changes. If even a small fraction of customers responds to high prices through lower demands, it
can have a large impact on the overall market. Plants that normally would run only a few hours
Oklahoma Restructuring Impact 21
are called on less often, while other plants see more use as customers increase purchases during
low-cost times. Average prices go down and profitability goes up. Real-time pricing can be
implemented whether or not restructuring occurs, although restructuring facilitates it through
competition and increased opportunities for change.
To explore the potential of real-time pricing, both in a regulated and market environment, we
recalculated the demand load profiles for each retail customer category. Using the real-time
prices shown in Figure 5, we raised or lowered the customer demand depending on how much
the price differed from the average price. We did not modify demands in the Other category,
which includes the wholesale exports and sales internal to Oklahoma that are not in the three
main categories.
We used a price elasticity of –0.10, meaning that a 10% increase in price reduces demand by
1%. There is little information on the correct value to use, although recent price changes in
California give a clue. In San Diego in the early summer of 2000, the local utility was allowed to
raise its prices to the market rates. A study of the impact on demand was conducted by James
Bushnell and Erin Mansur of the Program on Workable Energy Regulation (Bushnell and
Mansur 2001). They found that a doubling of rates resulted in a drop in demand of 2.2 to 7.6
percent. More recently, California had rate increases in the late spring of 2001 and saw a
reduction of demand of 12% comparing June 2001 use to June 2000 use, after taking out
weather-related factors (Hirsh and Kennedy, 2001). However, price changes differed between
customer categories, and a large amount of non-price-related incentives were also put in place.
Neither of these data conclusively provides information on customer response over a long
period. Generally, elasticity increases as customers become more familiar with the prices and
have the time to invest in equipment that will shift or reduce demand. So while San Diegans
responded with an elasticity factor between –0.02 and –0.08, given time they may increase their
responsiveness and raise the factor. A broader study on the impacts of increasing customer
responsiveness is included in our report on generation adequacy (Hirst and Hadley 2000).
Each customer category’s load shape changed slightly in response to the variable prices.
Figure 6 shows the change to the residential customer load shape in the peak and off-peak
season. Total energy purchases were kept the same but electricity use during the highest-priced
part of the peak season was reduced by 7.7% (570 MW) because of the high prices shown in
Figure 5. The Off-peak season saw very little change from the original load duration curve
because prices did not vary greatly. The commercial and industrial sectors saw slightly higher
percentage declines in their peak demands (9.6% and 11.6% respectively). This was mainly
because the price differential was greater for them since the fixed T&D cost component are a
lower proportion of their overall prices.
Oklahoma Restructuring Impact 22
Figure 6: Change in Residential Load Duration Curve from real-time pricing and elasticity
0
1000
2000
3000
4000
5000
6000
7000
8000
0 20 40 60 80 100
Percent of Season
Power Level, MW
Original Residential Peak
New Residential Peak
Original Residiential Off-peak
New Residential Off-peak
The result of changing each customer’s load profile was a reduction in retail peak demand of
1,240 MW. This represents a reduction of 9% in retail peak demands. Since exports and other
sales were not modified, the total drop was 6.1% of the original system peak demand. The
system load factor, which is the ratio of the average demand to the peak demand, improved from
57.4% to 61.1%. This means that while fewer plants may be needed to meet demand, those that
are used run for a longer part of the year.
We first ran ORCED with the new demands but same set of plants. This resulted in a reserve
margin of 15.4%, significantly higher than the 8.4% with the original demands. Customer prices
averaged 0.25¢/kWh lower than in the base scenario, but return on equity for plants dropped. The
peaker plants were most seriously affected. While new CC plants had ROE drop from 13.9% to
9.1%, the new CT plants dropped from 6.7% to 0.2% and the old CT plants dropped from +52%
to –56%.
Because of the lower demand, we removed two new CT’s from the list of plants, reducing
capacity by 620 MW. This still left a larger reserve margin than in the original case, 12.2%
versus 8.4%. Despite the increase in reserve margin, the total reliability of the two systems as
measured by the Loss of Load Probability is roughly the same. The extra reserve margin is
needed to make up for the possibility of other plants not being available when needed. With the
higher load factor there is a greater need for reserves to back up any plants with forced outages.
In addition to dropping the two plants, we lowered the fixed cost adder from 25% to 22% to
leave the CC plants with a 14% ROE. In this scenario, prices to consumers still dropped
Oklahoma Restructuring Impact 23
compared to the case without elasticity, by .09 ¢/kWh for regulated prices and .04 ¢/kWh for
market prices. With fewer plants, the returns on equity to the remaining plants stayed nearly the
same. New CC plants saw returns of 13.8%, new CT’s had 7.7%, and old CT’s had a 48% return.
The response of customers to real-time prices has a modest effect on average prices paid. Its
larger impact is on prices paid at the peak. In the case without elasticity impacts, market prices
were 120 ¢/kWh during the short time (~15 hours) when all plants were at full capacity. In the
case with elasticity this price peaked at 100 ¢/kWh. With elasticity and consequent flatter
demand profile, peak prices do not have to rise as much to lower demand to available capacity.
Oklahoma Restructuring Impact 25
5 Economic Analysis
5.1 Introduction
Electricity is a prominent product in the modern economy and a critical input into many
production processes, including those of households. The turmoil in California’s electric power
markets in late 2000 and early 2001 heightened national attention on this product.
In 1998, the value of private electricity production in Oklahoma was 2.3 percent of the total
value of production in the state. In that year, 40.7 percent of Oklahoma’s electricity generation
was used by residential consumers, 26 percent by commercial establishments, and 27.5 percent
by industrial consumers (EIA 2000c).
For this economic analysis we used the highest priced scenario, with all plants pricing at
market rates including coal and hydro (Table 10). Other scenarios, with prices much closer
between regulated and market-based, should show much less economic impact.
The average simulated rate increases of 5 percent for residential prices, 14 percent for
commercial, and 23 percent for industrial amount to a weighted average price increase of 12
percent. Thus, the economic analysis below examines a roughly 12-percent price increase in a
commodity that accounts for 2.3 percent of state production. Against this aggregate backdrop, it
is not surprising that the electricity rate changes identified with potential deregulation of
Oklahoma’s electric power industry have very small impacts on the overall economy of the state.
Depending on the price-change scenario, employment in the state could fall by three or four one-hundredths
of one percent while other property income could rise by about one-third of one
percent. The differences in impact across the scenarios also are small.
5.2 The Method of Analysis
The impact of changes in the three electricity rates was studied with 528-sector input-output
model of the Oklahoma economy.2 Input-output models trace the flows of expenditures through
the production sectors of an economy. Each production sector purchases produced inputs from
other industrial and commercial sectors (called intermediate inputs, or intermediate demand),
both within the state and outside it; hires labor; pays for the use of capital equipment; and pays
indirect business taxes. The labor receiving wages and salaries from each sector spends the
income they receive on the products of these industrial sectors, as well as on domestic and
foreign imports. These expenditures are called “final demands.” Some of the owners of the
capital equipment used in the industrial and commercial sectors live in the state and also spend
their income on locally produced goods and services. Thus there is a circular flow of income,
from production sectors hiring labor, through the spending of that labor income on locally and
externally produced products, with the demand for each product requiring inputs from many
other production sectors.
2 The IMPLAN model, with 1998 social accounts data for Oklahoma, was used in the analysis: Minnesota
IMPLAN Group, Inc., IMPLAN Professional, Version 2.0; Social Accounting & Impact Analysis Software
(Stillwater, Minn.: Minnesota Implan Group, April 1999).
Oklahoma Restructuring Impact 26
The usual method of studying economic impacts with an input-output model is to alter the
final demand for one or more products. This change injects additional income into the economy
at a particular point. That increase in final demand increases intermediate demands for the
products used to produce that good, and it also puts more wage and capital income into
circulation in the economy as more people and machinery are required to produce the additional
output. These dollars are spent as final demands across the entire array of goods produced in the
economy, and the increases in the final demands for these products increases the intermediate
demands for the other goods required to produce them. The money inserted into the economy
circulates through the production sectors and income-receiving households several times,
causing an expansion in the economy larger than the initial increase in final demand. However,
some of this additional income “leaks out” of the local economy during the spending cycles, as
people purchase goods imported from other states and countries, so that each new round of
spending gets smaller than the previous, as the purchases circulate through the economy.
The electricity price increases studied here have a different structure than the final demand
change usually posited for an input-output model. They are price changes rather than changes in
demands. In fact, in the short run, the quantity of electricity demanded, both by households and
firms, is quite inelastic, so that the final and intermediate demands for electricity change virtually
not at all. Instead, they cost more to produce. To accommodate this change to the input-output
framework, we altered the production structure of each sector, making each sector spend more on
electricity than before, and the set of final demands for households and other institutions (e.g.,
state and local government).
In changing the production structure of the industrial and commercial sectors, we recognized
that Oklahoma is thoroughly integrated into the United States economy, so that any changes in
its local production costs cannot affect the prices it pays for traded commodities.3 Similarly with
the labor and capital markets. Any increase in electricity prices must be offset exactly by
reductions in expenditures on other inputs, both other produced inputs and labor and capital.
Correspondingly in the expenditure patterns of households and institutions, we keep the initial
expenditures constant, taking the extra expenditures on electricity out of all other purchases, in
proportion to their expenditure shares in the budget. This imposes very nearly a zero elasticity of
demand for electricity on households.
In the first round of effects from the increase in electricity prices, industrial and commercial
producers reduce their intermediate demands for virtually all other inputs besides electricity, and
households and other institutions reduce their purchases of all products other than electricity. In
the next round, come reductions in intermediate purchases by all sectors other than electricity, in
3 Many services are nontraded, and their local prices could diverge somewhat from the prices of similar
products in other locations. However, national labor and capital markets, and national markets for the purchased
inputs of those activities, act to keep those prices rather close together across locations. The only input that
ultimately cannot be moved around to keep its price equalized across locations is land, and land rents will rise or fall
locally to absorb differences in prices of nontradable goods and services. An alternative approach to modeling the
production changes in each sector would have been to take the incremental electricity expenditure one hundred
percent out of the value-added category “other property income,” which includes land rents. Our method supposes
that producers manage to reduce expenditures proportionally across all their other inputs, rather than experiencing
the entire cost squeeze in the rents (residual profits) to land they use (Roback 1982).
Oklahoma Restructuring Impact 27
response to the direct retrenchments in employment and intermediate purchases. These are
indirect effects of the initial round of cuts. However, payments to the electricity sector increase
in the direct impact, and that sector’s demands for intermediate inputs and for labor and capital
rise, at least partially offsetting reductions elsewhere in the economy. The expanding electricity
sector, as well as expansions in several closely related industries, coal in particular, increases
labor income, which gets spent on the full array of goods and services in the economy. With
some sectors expanding and others contracting, the changes in income going to employees
induce a series of corresponding expansions and contractions in expenditures across all the
sectors.
In an input-output model, a state’s exports would change only in response to outsiders’
demands for them, although imports respond to both intermediate and final demand changes
originating within the state. Thus exports do not change simply because a state “can produce
more” of some good. Outside demanders must ask for more of specific goods the state produces.
We have not considered an increase in outside demands for electric power from Oklahoma
generators. A broader, regional study on the economic consequences of power production and
prices may be worthwhile in the future.
5.3 Numerical Results
We present impact results for three scenarios of electricity price changes. In the first scenario
(all plants, including hydro- and coal-generation, at market prices, including an adder 25 percent
of avoided cost), the residential rate rises by 5 percent, the commercial by 15 percent, and the
industrial by 23 percent (Table 12). The second scenario is similar but with the hydro plants
regulated. It has the residential rate rise by 4 percent, the commercial by 13 percent, and the
industrial by 21 percent. In the third scenario (all plants, including hydro- and coal-generation, at
market prices, customer demand shape changed due to elasticity), residential rate rises by 5
percent, the commercial by 16 percent, and the industrial by 26 percent. To gain some insight
into the contributions of these separate rate changes within any scenario, we apply the rate
changes separately for the third scenario, and find that in some cases the rate increases for
different customer classes have opposite effects in some sectors.
Table 12: Percent increase in prices market-based versus regulated, with most or all plants
at market rates
All plants (w/ coal and
hydro) at market prices
All plants but hydro at
market prices
All plants at market,
elasticity change demand
Residential 5 4 5
Commercial 15 13 16
Industrial 23 21 26
Table 13 and Table 14 report the output changes for individual industrial and commercial
sectors that are particularly strongly affected–although in absolute terms, all of the changes are
small, with the exception of those in the private electricity sector. Table 13 identifies the sectors
experiencing the strongest contractionary impacts, and Table 14 reports the comparable
information for the sectors undergoing the strongest expansions. The third scenario, which has
Oklahoma Restructuring Impact 28
the highest weighted-average rate change, has the strongest impacts, both negative and positive.
The first scenario, with the lowest weighted-average rate increase, has the smallest impacts
among contracting industries (Table 13), but generally has the second-largest impacts, after
scenario 3, among expanding industries (Table 14).
Table 13: Industrial sectors with output decreases, percents
industry
Scenario
1
% change
Scenario
2
% change
Scenario
3
% change
Scenario 3
Industrial
% change
Scenario 3
commercial
% change
Scenario 3
residential
% change
Explosives -0.595 -0.551 -0.679 -0.764 0.045 0.040
Logging camps &
logging contractors
-0.516 -0.474 -0.586 -0.615 0.008 0.020
Uranium, radium,
vanadium ores
-0.411 -0.376 -0.465 -0.468 0.001 0.002
Metal mining
services
-0.411 -0.376 -0.465 -0.468 0.001 0.002
Agriculture, forestry,
fishery services
-0.393 -0.357 -0.441 -0.416 -0.009 -0.016
Plastics, materials &
resins
-0.277 -0.253 -0.312 -0.311 -0.001 -0.000
Synthetic rubber -0.231 -0.210 -0.260 -0.258 -0.002 -0.000
Paperboard
containers & boxes
-0.216 -0.195 -0.241 -0.201 -0.030 -0.010
Wood pallets &
skids
-0.200 -0.181 -0.224 -0.184 -0.039 -0.001
State & local
government
education
-0.211 -0.180 -0.221 0.092 -0.341 0.028
Animal & marine fats
& oils
-0.180 -0.164 -0.203 -0.194 -0.004 -0.005
At the level of the individual industrial sectors, the change in the industrial rate tends to have
the strongest impact on contracting industries, as shown in Table 13, with the exception of the
state and local government production sector, which experiences the strongest contractionary
impact from the increase in the commercial rate. The commercial and residential rates have very
small impacts on these contracting sectors. Among the expanding sectors, shown in Table 14, the
increase in the industrial rate still tends to have the largest impact on the expansions, but the
magnitudes of the commercial and residential rate increases are much closer to those of the
industrial rate. The 10 to 11 percent expansion in the private electricity sector is in value terms,
not in terms of megawatt hours generated.
Oklahoma Restructuring Impact 29
Table 14: Industrial sectors with output increases, percents
industry Scenario
1
% change
Scenario
2
% change
Scenario
3
% change
Scenario 3
Industrial
% change
Scenario 3
commercial
% change
Scenario 3
residential
% change
maintenance &
repair, other
facilities
0.414 0.360 0.446 0.242 0.065 0.140
railroads & related
services
0.393 0.393 0.487 0.132 0.207 0.147
steam engines &
turbines
1.16 1.011 1.252 0.551 0.411 0.290
coal mining 1.71 1.489 1.844 0.785 0.624 0.435
federal electric
utilities
10.674 9.286 11.495 5.085 3.777 2.631
electric services 10.704 9.312 11.526 5.099 3.787 2.638
state & local electric
utilities
10.733 9.338 11.558 5.113 3.798 2.645
Aggregate impacts on categories of income are reported in Table 15. The separate
contributions of the different rate classes are somewhat different at the aggregate level than
among the outputs of the sectors reported individually. Employee compensation falls (which,
with a fixed wage, amounts to a reduction in employment) by a very small extent in all three
scenarios, but the driving force behind that reduction is the increase in the commercial rate. The
industrial and residential rate increases actually have minuscule positive effects on employment.
Household consumption increases by small amounts for all income groups. Lower income
groups experience slightly larger increases, with the exception of the highest income group,
which experiences a larger consumption increase than all but the two lowest income groups.
Increases in all three customer classes act to elevate consumption of the lowest three income
groups, but the increase in the commercial rate depresses consumption in households earning
$15,000 per year and above. Nevertheless, these are all very small changes and may be driven
primarily by how the IMPLAN model allocates wage and property income to households.
Claiming that these rate increases disproportionately affect different income groups on the basis
of this analysis would be exaggerated.
Proprietary income4 and other property income5 both rise, the former by about one tenth of
one percent, the latter by about one third to four-tenths of one percent. Indirect business taxes
also rise, by about the same percent as other property income. The relative contributions of
changes in the residential, commercial and industrial rates differ across these income groups.
State and local government non-educational activities increase by a considerable amount,
compared to the typical impacts of these rate changes. They respond positively to all three
categories of rate increase, while educational and investment respond negatively to the
4 Income to self-employed individuals, typically private business owners, doctors, lawyers, etc.
5 Income from interest, rents, royalties, dividends, and profits, including rents paid to individuals on property
and corporate profits earned by corporations.
Oklahoma Restructuring Impact 30
commercial rate increases, which reduces their overall sensitivity to the rate changes to virtually
nil. Capital investment also increases slightly in response to all the rate changes. It responds
positively to rate changes in all three customer classes, but somewhat more strongly to changes
in the industrial rates, probably reflecting increased demand for capital equipment in private
electricity generation and coal.
Table 15: Economic impacts of changes in electricity price schedules, percent change
Aggregate income
category
Scenari
o 1
%
Scenario
2
%
Scenario
3
%
Scenario 3
Industrial
%
Scenario 3
commercial
%
Scenario 3
residential
%
Employee
compensation
-0.039 -0.034 -0.042 0.005 -0.059 0.011
Proprietary income 0.102 0.086 0.106 0.026 0.032 0.049
Other property income 0.385 0.334 0.413 0.183 0.124 0.107
Indirect business taxes 0.329 0.287 0.355 0.178 0.087 0.0900
Household < $5k 0.128 0.112 0.139 0.069 0.035 0.035
Household $5-10k 0.084 0.073 0.090 0.047 0.019 0.024
Household $10-15k 0.064 0.056 0.069 0.040 0.006 0.023
Household $15-20k 0.056 0.048 0.060 0.041 -0.005 0.024
Household $20-30k 0.043 0.037 0.046 0.037 -0.015 0.024
Household $30-40k 0.040 0.035 0.043 0.036 -0.018 0.026
Household $40-50k 0.030 0.026 0.033 0.031 -0.023 0.024
Household $50-70k 0.024 0.021 0.026 0.028 -0.026 0.024
Household $70k+ 0.069 0.060 0.074 0.044 -0.003 0.034
State & local govern-ment,
non-education
0.144 0.127 0.157 0.092 0.037 0.028
State & local govern-ment,
education
-0.000 0.001 0.003 0.092 -0.117 0.028
State & local govern-ment,
investment
-0.000 0.001 0.003 0.092 -0.117 0.028
Capital investment 0.153 0.133 0.165 0.079 0.046 0.040
5.4 Conclusions of input/output analysis
The aggregate economic impacts of the electricity rate increases that appear likely to emerge
from deregulation as projected here are very small. These impact projections are likely to be on
the high side of actual, long-run impacts, since the assumptions of the input-output framework,
as well as assumptions we adopted for this study, minimize the opportunities to substitute away
from electricity in both final and intermediate demands. We did not attempt to simulate the
potential for substitution away from electricity into natural gas for some energy uses, but over a
five- to ten-year period, if some classes of rates stayed twenty to twenty-five percent higher,
some substitutions surely would occur in specific uses such as heating, air conditioning and
water heating. Additionally, the assumption made here to take the incremental electricity cost out
of all inputs instead of sinking them all into land rents, would tend to elevate the short-run
response to the rate increases through the indirect effects on demands for other intermediate
products.
Oklahoma Restructuring Impact 31
6 Results and Conclusion
Based on the analysis above, there are several important results for decision-makers in
Oklahoma. First, due to economics and transmission constraints, it is likely that some of the
proposed new plants will be cancelled or postponed. Second, existing low-cost coal and hydro
capacity will make high returns if allowed to price their production at the wholesale market rates,
at the expense of consumers. Based on the rationale of stranded costs as applied by FERC and
other states undergoing restructuring, it may be advisable to continue to have their production
priced at their cost including a reasonable return instead. Care must be taken to avoid market
manipulation if companies own both regulated and unregulated plants. Third, the economics of a
spot market for electricity pricing do not favor new plants with high capital costs, unless they can
incorporate some of their fixed costs into their bids and have sufficient market power to avoid
being tremendously undercut by competitors. Fourth, customer response to real-time prices can
serve to lower peak demands significantly. Less new construction is needed and prices are
reduced modestly. Fifth, even using the highest electricity price increases we modeled, the
overall economic effect on the state’s economy was slight. Employment decreased less than
0.05% overall and showed increases in some sectors, notably mining and the electric industry
itself.
6.1 Excess capacity and growth in exports
As described in Section 3, the amount of generating capacity planned for construction in
Oklahoma greatly exceeds the growth in demand. Even by 2010, internal demand is only
expected to rise around 26%, while in-state capacity is projected to double by 2004. While
expansion of power exports may consume some of this excess, it would have to expand from
current 1,000 MW to over 9,000 MW to utilize all of the capacity and leave a reasonable reserve
margin. Transmission capacity limits are likely to limit this export to 6,000 MW at the most,
assuming that other states have a need for this power.
In fact, there is a growing realization that the market may be set for a bust in the near future.
According to Christopher Ellinghaus, an investment banker at Williams Capital Group, power
companies across the country have proposed 350,000 MW of new plants by 2004, but only
100,000 MW of this is expected to actually be built (Bannerjee, 2001). According to the New
York Times article, transmission constraints and power plant economics are both playing a role
in the lowering of expectations. Many of the announcements of new capacity were based on the
expectation of broadly rising prices, as exemplified by California and the entire western region.
With the recent decline in wholesale prices, new plant economics are not as favorable.
Furthermore, many of the plants are being located in states with large gas resources, such as
Oklahoma and Louisiana. However, transmission systems are not being upgraded quickly
enough to be able to ship this excess capacity to states needing it. In Oklahoma, only one
additional 345 kV line is planned between now and 2010. Expansion of the transmission system
is more difficult to construct than new generation. Current transmission owners see little benefit
to build since it dilutes the value of their existing lines. The same goes for owners of plants in the
high-cost region. Landowners do not see the benefit since the power is to be used by others.
Even intervening states frequently object to new lines. For example, Connecticut recently vetoed
Oklahoma Restructuring Impact 32
a proposed transmission line to Long Island since they would not benefit from it and it may
disturb some oyster beds in the region (Behr 2001).
6.2 Regulation of Coal and Hydro
Existing coal and hydro plants have the good fortune of having low costs, both operating and
capital. They are and will remain a significant fraction of the overall capacity in the state (28% in
2010) but not enough that they become the marginal producers and consequent price setters. The
average price paid to coal plants over the year is 3.41 ¢/kWh while the coal and hydro average
costs are only 1.5 ¢/kWh (Table 9). Hydro plants are preferentially run during peak times so see
an even higher average price. If the plants receive these market prices, customers’ average prices
are higher by 0.74 ¢/kWh than if the plants received cost-based rates.
When some other states have restructured they provided that some power plants would
continue to be priced at their costs rather than sell at the market rates. The original rationale
behind the cost-based pricing was that it was thought that the utilities had some plants and
contracts with overall costs much higher than the market would be and deserved to recoup these
costs. An implied social contract existed in the past that utilities would be guaranteed a
reasonable return on prudent investments. If certain historical investments could not be
recovered in a restructured market, then they should be recouped through a “stranded cost” fee or
transition charge. Most states that have restructured implemented stranded cost recovery for their
utilities. The amount of recovery was set at the start of restructuring, with later true-ups as costs
became better known (Hirst and Hadley 1998).
Oklahoma is faced with the opposite situation of other states; its power plants, especially the
coal and hydro plants, have costs much lower than the market prices. It may be advisable that
this difference be returned to customers in some fashion, through cost-based pricing (such as
modeled here), through rebates following the sale of plants, or other mechanisms. However, the
mechanisms for these plants to continue selling at their cost rather than market must be carefully
constructed to avoid unintended consequences, misplaced incentives, or market manipulation.
6.3 Impact of Market Power
In a purely competitive market where supply bids into a market until demand is satisfied, the
optimum bid for any supplier is to bid at their marginal cost. Prices then are set by the highest
price bid that fulfills demand. This was described in more detail in our Phase I report. The
problem for the electric industry is that in an industry with a high ratio of fixed to variable costs,
there is a greater likelihood that the resulting prices will not cover their fixed costs, leading to
boom and bust cycles. Examples include such industries as airlines, steel, and cement. In many
such industries, what happens is a build-up of inventories that leads to temporary plant closures
as demand and supply equilibrate. The lack of an economical electrical storage mechanism
makes this process more problematic for the electric generation business. The inelasticity of
supply and demand can lead to great volatility.
This problem is especially acute if a large fraction of the suppliers has similar marginal costs.
If one supplier tries to include fixed costs in its bid at any given time, another supplier can price
slightly below this (but still above their marginal cost) and take the sale. With many suppliers,
Oklahoma Restructuring Impact 33
this rationale drives the price down to the plants’ marginal costs and none of them recover their
fixed costs.
One definition of market power is the ability to price goods above the competitive level and
make those prices stick. This can only happen if a supplier or group of suppliers have a large
enough share of the market and that customers do not have a ready substitute for the product.
In our scenarios, we showed how if all suppliers price at their marginal costs, then the new CC
and CT plants lose money. We then used a simple model of market power where all suppliers
incorporated 25% of their fixed costs (including capital costs) in their bid prices. This simple
formula provided most new plants with sufficient income to justify their construction, while
providing many older plants with large profits. A more complex and realistic form of market
power was modeled by assuming that some low cost technologies raised their prices to just
below the level of the next major technology, but others just priced on their margin. While this
improved the revenue for these low-cost technologies, it was not sufficient for them to fully
cover their fixed costs. They had to have the more expensive technologies also raise their bids so
that all would gain revenue, at the expense of consumers. Furthermore, if any one plant within
the technology grouping broke ranks and lowered their bid, then they earned much more
revenue. This argues against a strong amount of market power through pricing strategies.
The other form of market power we considered was the ability of one company to withhold
capacity from its lower cost plants in order to increase the prices and net revenue received by its
remaining plants. This form of market power proved more successful in our examples. When the
owner of two CC plants lowered the operation of one plant by 10%, the sales increased slightly
for the other. But more important, the prices that all plants received increased such that the
owner of the two plants had higher profits. For the company that owned both regulated and
unregulated plants, withholding capacity proved even more successful. The company earned
more on its unregulated plants through increased sales and prices, and the coal plant continued to
earn its regulated return even though it produced 10% less.
Our modeling overstates the market power influence of the plants because we only model the
plants within Oklahoma. In reality, power plants from other states may offset the lost production
of these plants, so that prices would not rise as much. The influence of these other plants would
require a broader regional study of the power system.
6.4 Impact of Price Elasticity
Customer response to real-time prices can lower the peak demands significantly, by 8% or
more. This lowers the amount of capacity needed to meet demands within Oklahoma. This can
free up the capacity for external sales (if transmission capacity exists) or lessens the need for new
plants. It has a small effect on the average prices paid, because the largest impact is on the small
part of the year when demand is highest. If customers simply shift their demands to other times,
then total sales are not affected. Suppliers may want to adjust their pricing to reflect the change
in plant utilization, lowering the amount they need to raise their bidding to recover fixed costs.
Oklahoma Restructuring Impact 34
6.5 Economic impact on state
The aggregate economic impacts of the range of electricity rate increases derived in this study
would be very small. Depending on the rate scenario, aggregate employment in the state would
fall by as much as 0.042 percent or by as little as 0.034 percent. Neither change would be
detectable in routine employment statistics. Outputs in industrial and commercial sectors not
intuitively related intimately to the electricity sector are affected by correspondingly small
percentages—by roughly one-half of one percent either up or down. The output of coal mining
increases by 1.7 to 1.8 percent, depending on the rate scenario, while the value of the private
electricity sector’s output increases by 9 to 11.5 percent, which is roughly the weighted-average
price change of unchanged megawatts of generation. Alternative assumptions used in the
economic analysis probably would yield even smaller impacts.
6.6 Conclusions
The economic impact of restructuring the electric power industry could be relatively modest
or could raise prices to consumers. A key difference will be how the restructuring takes place:
what plants are included in restructuring, how costs or prices are communicated to consumers,
and whether capacity additions are in line with expected growth in demands.
Any restructuring must take into account that many of the existing plants have costs well
below market rates. The difference between cost and market prices are currently received by
consumers since the plants’ production is priced at cost plus a reasonable return. Policy-makers
will need to address how this future price and cost difference is shared between the state’s
consumers and the owners of the facilities.
It appears that the announced new plants to be constructed in the state are well in excess of the
internal needs of the state and more than the transmission system can effectively export. Delays
or cancellations are likely in order to prevent a glut on the market. Customer response to real-time
prices and competition in external markets could further reduce the need for new plants.
Information such as this study, and evaluation of the market by developers and the OCC, should
help to avoid the worst of any market volatility due to an imbalance between supply and demand.
Oklahoma Restructuring Impact 35
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